Po Hu, Minlie Huang, Peng Xu, Weichang Li, A. Usadi, Xiaoyan Zhu
Though news readers can easily access a large number of news articles from the Internet, they can be overwhelmed by the quantity of information available, making it hard to get a concise, global picture of a news topic. In this paper we propose a novel method to address this problem. Given a set of articles for a given news topic, the proposed method models theme variation through time and identifies the breakpoints, which are time points when decisive changes occur. For each breakpoint, a brief summary is automatically constructed based on articles associated with the particular time point. Summaries are then ordered chronologically to form a timeline overview of the news topic. In this fashion, readers can easily track various news topics efficiently. We have conducted experiments on 15 popular topics in 2010. Empirical experiments show the effectiveness of our approach and its advantages over other approaches.
{"title":"Generating Breakpoint-based Timeline Overview for News Topic Retrospection","authors":"Po Hu, Minlie Huang, Peng Xu, Weichang Li, A. Usadi, Xiaoyan Zhu","doi":"10.1109/ICDM.2011.71","DOIUrl":"https://doi.org/10.1109/ICDM.2011.71","url":null,"abstract":"Though news readers can easily access a large number of news articles from the Internet, they can be overwhelmed by the quantity of information available, making it hard to get a concise, global picture of a news topic. In this paper we propose a novel method to address this problem. Given a set of articles for a given news topic, the proposed method models theme variation through time and identifies the breakpoints, which are time points when decisive changes occur. For each breakpoint, a brief summary is automatically constructed based on articles associated with the particular time point. Summaries are then ordered chronologically to form a timeline overview of the news topic. In this fashion, readers can easily track various news topics efficiently. We have conducted experiments on 15 popular topics in 2010. Empirical experiments show the effectiveness of our approach and its advantages over other approaches.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124956481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ye Jin, Sriram Lakshminarasimhan, Neil Shah, Zhenhuan Gong, Choong-Seock Chang, Jackie H. Chen, S. Ethier, H. Kolla, S. Ku, S. Klasky, R. Latham, R. Ross, K. Schuchardt, N. Samatova
The growing gap between the massive amounts of data generated by petascale scientific simulation codes and the capability of system hardware and software to effectively analyze this data necessitates data reduction. Yet, the increasing data complexity challenges most, if not all, of the existing data compression methods. In fact, loss less compression techniques offer no more than 10% reduction on scientific data that we have experience with, which is widely regarded as effectively incompressible. To bridge this gap, in this paper, we advocate a transformative strategy that enables fast, accurate, and multi-fold reduction of double-precision floating-point scientific data. The intuition behind our method is inspired by an effective use of preconditioners for linear algebra solvers optimized for a particular class of computational "dwarfs" (e.g., dense or sparse matrices). Focusing on a commonly used multi-resolution wavelet compression technique as the underlying "solver" for data reduction we propose the S-preconditioner, which transforms scientific data into a form with high global regularity to ensure a significant decrease in the number of wavelet coefficients stored for a segment of data. Combined with the subsequent EQ-$calibrator, our resultant method (called S-Preconditioned EQ-Calibrated Wavelets (SW)), robustly achieved a 4-to 5-fold data reduction-while guaranteeing user-defined accuracy of reconstructed data to be within 1% point-by-point relative error, lower than 0.01 Normalized RMSE, and higher than 0.99 Pearson Correlation. In this paper, we show the results we obtained by testing our method on six petascale simulation codes including fusion, combustion, climate, astrophysics, and subsurface groundwater in addition to 13 publicly available scientific datasets. We also demonstrate that application-driven data mining tasks performed on decompressed variables or their derived quantities produce results of comparable quality with the ones for the original data.
{"title":"S-preconditioner for Multi-fold Data Reduction with Guaranteed User-Controlled Accuracy","authors":"Ye Jin, Sriram Lakshminarasimhan, Neil Shah, Zhenhuan Gong, Choong-Seock Chang, Jackie H. Chen, S. Ethier, H. Kolla, S. Ku, S. Klasky, R. Latham, R. Ross, K. Schuchardt, N. Samatova","doi":"10.1109/ICDM.2011.138","DOIUrl":"https://doi.org/10.1109/ICDM.2011.138","url":null,"abstract":"The growing gap between the massive amounts of data generated by petascale scientific simulation codes and the capability of system hardware and software to effectively analyze this data necessitates data reduction. Yet, the increasing data complexity challenges most, if not all, of the existing data compression methods. In fact, loss less compression techniques offer no more than 10% reduction on scientific data that we have experience with, which is widely regarded as effectively incompressible. To bridge this gap, in this paper, we advocate a transformative strategy that enables fast, accurate, and multi-fold reduction of double-precision floating-point scientific data. The intuition behind our method is inspired by an effective use of preconditioners for linear algebra solvers optimized for a particular class of computational \"dwarfs\" (e.g., dense or sparse matrices). Focusing on a commonly used multi-resolution wavelet compression technique as the underlying \"solver\" for data reduction we propose the S-preconditioner, which transforms scientific data into a form with high global regularity to ensure a significant decrease in the number of wavelet coefficients stored for a segment of data. Combined with the subsequent EQ-$calibrator, our resultant method (called S-Preconditioned EQ-Calibrated Wavelets (SW)), robustly achieved a 4-to 5-fold data reduction-while guaranteeing user-defined accuracy of reconstructed data to be within 1% point-by-point relative error, lower than 0.01 Normalized RMSE, and higher than 0.99 Pearson Correlation. In this paper, we show the results we obtained by testing our method on six petascale simulation codes including fusion, combustion, climate, astrophysics, and subsurface groundwater in addition to 13 publicly available scientific datasets. We also demonstrate that application-driven data mining tasks performed on decompressed variables or their derived quantities produce results of comparable quality with the ones for the original data.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124631528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we explore how to use ranking SVM to train the feature weights for query-focused multi-document summarization. To apply a supervised learning method to sentence extraction in multi-document summarization, we need to derive the sentence labels for training corpus from the existing human labeling data in form of. However, this process is not trivial, because the human summaries are abstractive, and do not necessarily well match the sentences in the documents. In this paper, we try to address the above problem from the following two aspects. First, we make use of sentence-to-sentence relationships to better estimate the probability of a sentence in the document set to be a summary sentence. Second, to make the derived training data less sensitive, we adopt a cost sensitive loss in the ranking SVM's objective function. The experimental results demonstrate the effectiveness of our proposed method.
{"title":"Learning to Rank for Query-Focused Multi-document Summarization","authors":"Chao Shen, Tao Li","doi":"10.1109/ICDM.2011.91","DOIUrl":"https://doi.org/10.1109/ICDM.2011.91","url":null,"abstract":"In this paper, we explore how to use ranking SVM to train the feature weights for query-focused multi-document summarization. To apply a supervised learning method to sentence extraction in multi-document summarization, we need to derive the sentence labels for training corpus from the existing human labeling data in form of. However, this process is not trivial, because the human summaries are abstractive, and do not necessarily well match the sentences in the documents. In this paper, we try to address the above problem from the following two aspects. First, we make use of sentence-to-sentence relationships to better estimate the probability of a sentence in the document set to be a summary sentence. Second, to make the derived training data less sensitive, we adopt a cost sensitive loss in the ranking SVM's objective function. The experimental results demonstrate the effectiveness of our proposed method.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116255561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To alleviate the memory and computational burdens of spectral clustering for large scale problems, some kind of low-rank matrix approximation is usually employed. Nyström method is an efficient technique to generate low rank matrix approximation and its most important aspect is sampling. The matrix approximation errors of several sampling schemes have been theoretically analyzed for a number of learning tasks. However, the impact of matrix approximation error on the clustering performance of spectral clustering has not been studied. In this paper, we firstly analyze the performance of Nyström method in terms of cluster ability, thus answer the impact of matrix approximation error on the clustering performance of spectral clustering. Our analysis immediately suggests an incremental sampling scheme for the Nyström method based spectral clustering. Experimental results show that the proposed incremental sampling scheme outperforms existing sampling schemes on various clustering tasks and image segmentation applications, and its efficiency is comparable with existing sampling schemes.
{"title":"Clusterability Analysis and Incremental Sampling for Nyström Extension Based Spectral Clustering","authors":"Xianchao Zhang, Quanzeng You","doi":"10.1109/ICDM.2011.35","DOIUrl":"https://doi.org/10.1109/ICDM.2011.35","url":null,"abstract":"To alleviate the memory and computational burdens of spectral clustering for large scale problems, some kind of low-rank matrix approximation is usually employed. Nyström method is an efficient technique to generate low rank matrix approximation and its most important aspect is sampling. The matrix approximation errors of several sampling schemes have been theoretically analyzed for a number of learning tasks. However, the impact of matrix approximation error on the clustering performance of spectral clustering has not been studied. In this paper, we firstly analyze the performance of Nyström method in terms of cluster ability, thus answer the impact of matrix approximation error on the clustering performance of spectral clustering. Our analysis immediately suggests an incremental sampling scheme for the Nyström method based spectral clustering. Experimental results show that the proposed incremental sampling scheme outperforms existing sampling schemes on various clustering tasks and image segmentation applications, and its efficiency is comparable with existing sampling schemes.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115810969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, semi-supervised clustering (SSC) has aroused considerable interests from the machine learning and data mining communities. In this paper, we propose a novel semi-supervised clustering approach with enhanced spectral embedding (ESE) which not only considers structure information contained in data sets but also makes use of prior side information such as pair wise constraints. Specially, we first construct a symmetry-favored k-NN graph which is highly robust to noisy objects and can reflect the underlying manifold structure of data. Then we learn the enhanced spectral embedding towards an ideal representation as consistent with the pair wise constraints as possible. Finally, through taking advantage of Laplacian regularization, we formulate learning spectral representation as semi definite-quadratic-linear programs (SQLPs) under the squared loss function or small semi definitive programs (SDPs) under the hinge loss function, which both can be efficiently solved. Experimental results on a variety of synthetic and real-world data sets show that our approach outperforms the state-of-the-art SSC algorithms on both vector-based and graph-based clustering.
{"title":"Learning Spectral Embedding for Semi-supervised Clustering","authors":"Fanhua Shang, Yuanyuan Liu, Fei Wang","doi":"10.1109/ICDM.2011.89","DOIUrl":"https://doi.org/10.1109/ICDM.2011.89","url":null,"abstract":"In recent years, semi-supervised clustering (SSC) has aroused considerable interests from the machine learning and data mining communities. In this paper, we propose a novel semi-supervised clustering approach with enhanced spectral embedding (ESE) which not only considers structure information contained in data sets but also makes use of prior side information such as pair wise constraints. Specially, we first construct a symmetry-favored k-NN graph which is highly robust to noisy objects and can reflect the underlying manifold structure of data. Then we learn the enhanced spectral embedding towards an ideal representation as consistent with the pair wise constraints as possible. Finally, through taking advantage of Laplacian regularization, we formulate learning spectral representation as semi definite-quadratic-linear programs (SQLPs) under the squared loss function or small semi definitive programs (SDPs) under the hinge loss function, which both can be efficiently solved. Experimental results on a variety of synthetic and real-world data sets show that our approach outperforms the state-of-the-art SSC algorithms on both vector-based and graph-based clustering.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116025355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel structure preserving over sampling (SPO) technique for classifying imbalanced time series data. SPO generates synthetic minority samples based on multivariate Gaussian distribution by estimating the covariance structure of the minority class and regularizing the unreliable eigen spectrum. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen feature dimensions, the synthetic samples expand effectively into the void area in the data space without being too closely tied with existing minority-class samples. Extensive experiments based on several public time series datasets demonstrate that our proposed SPO in conjunction with support vector machines can achieve better performances than existing over sampling methods and state-of-the-art methods in time series classification.
{"title":"SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification","authors":"Hong Cao, Xiaoli Li, D. Woon, See-Kiong Ng","doi":"10.1109/ICDM.2011.137","DOIUrl":"https://doi.org/10.1109/ICDM.2011.137","url":null,"abstract":"This paper presents a novel structure preserving over sampling (SPO) technique for classifying imbalanced time series data. SPO generates synthetic minority samples based on multivariate Gaussian distribution by estimating the covariance structure of the minority class and regularizing the unreliable eigen spectrum. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen feature dimensions, the synthetic samples expand effectively into the void area in the data space without being too closely tied with existing minority-class samples. Extensive experiments based on several public time series datasets demonstrate that our proposed SPO in conjunction with support vector machines can achieve better performances than existing over sampling methods and state-of-the-art methods in time series classification.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124601823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semi-supervised nonnegative matrix factorization (NMF)receives more and more attention in text mining field. The semi-supervised NMF methods can be divided into two types, one is based on the explicit category labels, the other is based on the pair wise constraints including must-link and cannot-link. As it is hard to obtain the category labels in some tasks, the latter one is more widely used in real applications. To date, all the constrained NMF methods treat the must-link and cannot-link constraints in a same way. However, these two kinds of constraints play different roles in NMF clustering. Thus a novel constrained NMF method is proposed in this paper. In the new method, must-link constraints are used to control the distance of the data in the compressed form, and cannot-ink constraints are used to control the encoding factor. Experimental results on real-world text data sets have shown the good performance of the proposed method.
{"title":"Text Clustering via Constrained Nonnegative Matrix Factorization","authors":"Yan Zhu, L. Jing, Jian Yu","doi":"10.1109/ICDM.2011.143","DOIUrl":"https://doi.org/10.1109/ICDM.2011.143","url":null,"abstract":"Semi-supervised nonnegative matrix factorization (NMF)receives more and more attention in text mining field. The semi-supervised NMF methods can be divided into two types, one is based on the explicit category labels, the other is based on the pair wise constraints including must-link and cannot-link. As it is hard to obtain the category labels in some tasks, the latter one is more widely used in real applications. To date, all the constrained NMF methods treat the must-link and cannot-link constraints in a same way. However, these two kinds of constraints play different roles in NMF clustering. Thus a novel constrained NMF method is proposed in this paper. In the new method, must-link constraints are used to control the distance of the data in the compressed form, and cannot-ink constraints are used to control the encoding factor. Experimental results on real-world text data sets have shown the good performance of the proposed method.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129743340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many clustering methods have been proposed for analyzing the relations inside networks with complex structures. Some of them can detect a mixture of assortative and disassortative structures in networks. All these methods are based on the fact that the entire network is observable. However, in the real world, the entities in networks, for example a social network, may be private, and thus, cannot be observed. We focus on private peer-to-peer networks in which all vertices are independent and private, and each vertex only knows about itself and its neighbors. We propose a privacy-preserving Gibbs sampling for clustering these types of private networks and detecting their mixed structures without revealing any private information about any individual entity. Moreover, the running cost of our method is related only to the number of clusters and the maximum degree, but is nearly independent of the number of vertices in the entire network.
{"title":"Secure Clustering in Private Networks","authors":"Bin Yang, Issei Sato, Hiroshi Nakagawa","doi":"10.1109/ICDM.2011.127","DOIUrl":"https://doi.org/10.1109/ICDM.2011.127","url":null,"abstract":"Many clustering methods have been proposed for analyzing the relations inside networks with complex structures. Some of them can detect a mixture of assortative and disassortative structures in networks. All these methods are based on the fact that the entire network is observable. However, in the real world, the entities in networks, for example a social network, may be private, and thus, cannot be observed. We focus on private peer-to-peer networks in which all vertices are independent and private, and each vertex only knows about itself and its neighbors. We propose a privacy-preserving Gibbs sampling for clustering these types of private networks and detecting their mixed structures without revealing any private information about any individual entity. Moreover, the running cost of our method is related only to the number of clusters and the maximum degree, but is nearly independent of the number of vertices in the entire network.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128237972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present in this paper a new clustering algorithm for multivariate data. This algorithm, called BOOL (Binary coding Oriented clustering), can detect arbitrarily shaped clusters and is noise tolerant. BOOL handles data using a two-step procedure: data points are first discretized and represented as binary words, clusters are then iteratively constructed by agglomerating smaller clusters using this representation. This latter step is carried out with linear complexity by sorting such binary representations, which results in dramatic speedups when compared with other techniques. Experiments show that BOOL is faster than K-means, and about two to three orders of magnitude faster than two state-of-the-art algorithms that can detect non-convex clusters of arbitrary shapes. We also show that BOOL's results are robust to changes in parameters, whereas most algorithms for arbitrarily shaped clusters are known to be overly sensitive to such changes. The key to the robustness of BOOL is the hierarchical structure of clusters that is introduced automatically by increasing the accuracy of the discretization.
{"title":"A Fast and Flexible Clustering Algorithm Using Binary Discretization","authors":"M. Sugiyama, Akihiro Yamamoto","doi":"10.1109/ICDM.2011.9","DOIUrl":"https://doi.org/10.1109/ICDM.2011.9","url":null,"abstract":"We present in this paper a new clustering algorithm for multivariate data. This algorithm, called BOOL (Binary coding Oriented clustering), can detect arbitrarily shaped clusters and is noise tolerant. BOOL handles data using a two-step procedure: data points are first discretized and represented as binary words, clusters are then iteratively constructed by agglomerating smaller clusters using this representation. This latter step is carried out with linear complexity by sorting such binary representations, which results in dramatic speedups when compared with other techniques. Experiments show that BOOL is faster than K-means, and about two to three orders of magnitude faster than two state-of-the-art algorithms that can detect non-convex clusters of arbitrary shapes. We also show that BOOL's results are robust to changes in parameters, whereas most algorithms for arbitrarily shaped clusters are known to be overly sensitive to such changes. The key to the robustness of BOOL is the hierarchical structure of clusters that is introduced automatically by increasing the accuracy of the discretization.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132041207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Along with the ever-growing Web, horror contents sharing in the Internet has interfered with our daily life and affected our, especially children's, health. Therefore horror image recognition is becoming more important for web objectionable content filtering. This paper presents a novel context-aware multi-instance learning (CMIL) model for this task. This work is distinguished by three key contributions. Firstly, the traditional multi-instance learning is extended to context-aware multi-instance learning model through integrating an undirected graph in each bag that represents contextual relationships among instances. Secondly, by introducing a novel energy function, a heuristic optimization algorithm based on Fuzzy Support Vector Machine (FSVM) is given out to find the optimal classifier on CMIL. Finally, the CMIL is applied to recognize horror images. Experimental results on an image set collected from the Internet show that the proposed method is effective on horror image recognition.
{"title":"Web Horror Image Recognition Based on Context-Aware Multi-instance Learning","authors":"Bing Li, Weihua Xiong, Weiming Hu","doi":"10.1109/ICDM.2011.155","DOIUrl":"https://doi.org/10.1109/ICDM.2011.155","url":null,"abstract":"Along with the ever-growing Web, horror contents sharing in the Internet has interfered with our daily life and affected our, especially children's, health. Therefore horror image recognition is becoming more important for web objectionable content filtering. This paper presents a novel context-aware multi-instance learning (CMIL) model for this task. This work is distinguished by three key contributions. Firstly, the traditional multi-instance learning is extended to context-aware multi-instance learning model through integrating an undirected graph in each bag that represents contextual relationships among instances. Secondly, by introducing a novel energy function, a heuristic optimization algorithm based on Fuzzy Support Vector Machine (FSVM) is given out to find the optimal classifier on CMIL. Finally, the CMIL is applied to recognize horror images. Experimental results on an image set collected from the Internet show that the proposed method is effective on horror image recognition.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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