Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168157
Z. Gong, P. Zhong, Yang Yu, Jiaxin Shan, W. Hu
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected. To solve the problem, a multi-scale CNN which can extract multi-scale features is designed for hyperspectral image classification. Furthermore, D-DSML, a diversified metric, is proposed to further improve the representational ability of deep methods. In this paper, a D-DSML-MSCNN method, which jointly learns deep multi-scale features and diversified metrics for hyperspectral image classification, is proposed to take both advantages of D-DSML and MSCNN. Experiments are conducted on Pavia University data to show the effectiveness of our method for hyperspectral image classification. The results show the advantage of our method when compared with other recent results.
{"title":"Joint learning of deep multi-scale features and diversified metrics for hyperspectral image classification","authors":"Z. Gong, P. Zhong, Yang Yu, Jiaxin Shan, W. Hu","doi":"10.1109/MLSP.2017.8168157","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168157","url":null,"abstract":"Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected. To solve the problem, a multi-scale CNN which can extract multi-scale features is designed for hyperspectral image classification. Furthermore, D-DSML, a diversified metric, is proposed to further improve the representational ability of deep methods. In this paper, a D-DSML-MSCNN method, which jointly learns deep multi-scale features and diversified metrics for hyperspectral image classification, is proposed to take both advantages of D-DSML and MSCNN. Experiments are conducted on Pavia University data to show the effectiveness of our method for hyperspectral image classification. The results show the advantage of our method when compared with other recent results.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"67 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80954721","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168109
Laurens Bliek, M. Verhaegen, S. Wahls
We propose CDONE, a convex version of the DONE algorithm. DONE is a derivative-free online optimization algorithm that uses surrogate modeling with noisy measurements to find a minimum of objective functions that are expensive to evaluate. Inspired by their success in deep learning, CDONE makes use of rectified linear units, together with a nonnegativity constraint to enforce convexity of the surrogate model. This leads to a sparse and cheap to evaluate surrogate model of the unknown optimization objective that is still accurate and that can be minimized with convex optimization algorithms. The CDONE algorithm is demonstrated on a toy example and on the problem of hyper-parameter optimization for a deep learning example on handwritten digit classification.
{"title":"Online function minimization with convex random relu expansions","authors":"Laurens Bliek, M. Verhaegen, S. Wahls","doi":"10.1109/MLSP.2017.8168109","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168109","url":null,"abstract":"We propose CDONE, a convex version of the DONE algorithm. DONE is a derivative-free online optimization algorithm that uses surrogate modeling with noisy measurements to find a minimum of objective functions that are expensive to evaluate. Inspired by their success in deep learning, CDONE makes use of rectified linear units, together with a nonnegativity constraint to enforce convexity of the surrogate model. This leads to a sparse and cheap to evaluate surrogate model of the unknown optimization objective that is still accurate and that can be minimized with convex optimization algorithms. The CDONE algorithm is demonstrated on a toy example and on the problem of hyper-parameter optimization for a deep learning example on handwritten digit classification.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"17 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90240796","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168175
Patrick Blöbaum, Shohei Shimizu
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the predictors and the actual prediction have not been considered. Here, we connect the underlying causal structure of a data generation process and the causal structure of a prediction mechanism. To achieve this, we propose a framework that identifies the feature with the greatest causal influence on the prediction and estimates the necessary causal intervention of a feature such that a desired prediction is obtained. The general concept of the framework has no restrictions regarding data linearity; however, we focus on an implementation for linear data here. The framework applicability is evaluated using artificial data and demonstrated using real-world data.
{"title":"Estimation of interventional effects of features on prediction","authors":"Patrick Blöbaum, Shohei Shimizu","doi":"10.1109/MLSP.2017.8168175","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168175","url":null,"abstract":"The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the predictors and the actual prediction have not been considered. Here, we connect the underlying causal structure of a data generation process and the causal structure of a prediction mechanism. To achieve this, we propose a framework that identifies the feature with the greatest causal influence on the prediction and estimates the necessary causal intervention of a feature such that a desired prediction is obtained. The general concept of the framework has no restrictions regarding data linearity; however, we focus on an implementation for linear data here. The framework applicability is evaluated using artificial data and demonstrated using real-world data.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"20 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88589018","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168118
Anshul Thakur, V. Abrol, Pulkit Sharma, Padmanabhan Rajan
In this paper we describe a semi-supervised algorithm to segment bird vocalizations using matrix factorization and Rényi entropy based mutual information. Singular value decomposition (SVD) is applied on pooled time-frequency representations of bird vocalizations to learn basis vectors. By utilizing only a few of the bases, a compact feature representation is obtained for input test data. Rényi entropy based mutual information is calculated between feature representations of consecutive frames. After some simple post-processing, a threshold is used to reliably distinguish bird vocalizations from other sounds. The algorithm is evaluated on the field recordings of different bird species and different SNR conditions. The results highlight the effectiveness of the proposed method in all SNR conditions, improvements over other methods, and its generality.
{"title":"Rényi entropy based mutual information for semi-supervised bird vocalization segmentation","authors":"Anshul Thakur, V. Abrol, Pulkit Sharma, Padmanabhan Rajan","doi":"10.1109/MLSP.2017.8168118","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168118","url":null,"abstract":"In this paper we describe a semi-supervised algorithm to segment bird vocalizations using matrix factorization and Rényi entropy based mutual information. Singular value decomposition (SVD) is applied on pooled time-frequency representations of bird vocalizations to learn basis vectors. By utilizing only a few of the bases, a compact feature representation is obtained for input test data. Rényi entropy based mutual information is calculated between feature representations of consecutive frames. After some simple post-processing, a threshold is used to reliably distinguish bird vocalizations from other sounds. The algorithm is evaluated on the field recordings of different bird species and different SNR conditions. The results highlight the effectiveness of the proposed method in all SNR conditions, improvements over other methods, and its generality.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"101 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91553228","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168124
Siqi Ye, S. Ravishankar, Y. Long, J. Fessler
Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical model. We propose a new formulation for low-dose CT image reconstruction based on a shifted-Poisson model based likelihood function and a data-adaptive regularizer using the sparsifying transform model for images. The sparsifying transform is pre-learned from a dataset of patches extracted from CT images. The nonconvex cost function of the proposed penalized-likelihood reconstruction with sparsifying transforms regularizer (PL-ST) is optimized by alternating between a sparse coding step and an image update step. The image update step deploys a series of convex quadratic majorizers that are optimized using a relaxed linearized augmented Lagrangian method with ordered-subsets, reducing the number of (expensive) forward and backward projection operations. Numerical experiments show that for low dose levels, the proposed data-driven PL-ST approach outperforms prior methods employing a nonadaptive edge-preserving regularizer. PL-ST also outperforms prior PWLS-ST approach at very low X-ray doses.
{"title":"Adaptive sparse modeling and shifted-poisson likelihood based approach for low-dosect image reconstruction","authors":"Siqi Ye, S. Ravishankar, Y. Long, J. Fessler","doi":"10.1109/MLSP.2017.8168124","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168124","url":null,"abstract":"Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical model. We propose a new formulation for low-dose CT image reconstruction based on a shifted-Poisson model based likelihood function and a data-adaptive regularizer using the sparsifying transform model for images. The sparsifying transform is pre-learned from a dataset of patches extracted from CT images. The nonconvex cost function of the proposed penalized-likelihood reconstruction with sparsifying transforms regularizer (PL-ST) is optimized by alternating between a sparse coding step and an image update step. The image update step deploys a series of convex quadratic majorizers that are optimized using a relaxed linearized augmented Lagrangian method with ordered-subsets, reducing the number of (expensive) forward and backward projection operations. Numerical experiments show that for low dose levels, the proposed data-driven PL-ST approach outperforms prior methods employing a nonadaptive edge-preserving regularizer. PL-ST also outperforms prior PWLS-ST approach at very low X-ray doses.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"86 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85993930","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168184
Ryoma Tani, Hideyuki Watanabe, S. Katagiri, M. Ohsaki
Unlike Support Vector Machine (SVM), Kernel Minimum Classification Error (KMCE) training frees kernels from training samples and jointly optimizes weights and kernel locations. Focusing on this feature of KMCE training, we propose a new method for developing compact (small scale but highly accurate) kernel classifiers by applying KMCE training to support vectors (SVs) that are selected (based on the weight vector norm) from the original SVs produced by the Multi-class SVM (MSVM). We evaluate our proposed method in four classification tasks and clearly demonstrate its effectiveness: only a 3% drop in classification accuracy (from 99.1 to 89.1%) with just 10% of the original SVs. In addition, we mathematically reveal that the value of MSVM's kernel weight indicates the geometric relation between a training sample and margin boundaries.
{"title":"Compact kernel classifiers trained with minimum classification error criterion","authors":"Ryoma Tani, Hideyuki Watanabe, S. Katagiri, M. Ohsaki","doi":"10.1109/MLSP.2017.8168184","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168184","url":null,"abstract":"Unlike Support Vector Machine (SVM), Kernel Minimum Classification Error (KMCE) training frees kernels from training samples and jointly optimizes weights and kernel locations. Focusing on this feature of KMCE training, we propose a new method for developing compact (small scale but highly accurate) kernel classifiers by applying KMCE training to support vectors (SVs) that are selected (based on the weight vector norm) from the original SVs produced by the Multi-class SVM (MSVM). We evaluate our proposed method in four classification tasks and clearly demonstrate its effectiveness: only a 3% drop in classification accuracy (from 99.1 to 89.1%) with just 10% of the original SVs. In addition, we mathematically reveal that the value of MSVM's kernel weight indicates the geometric relation between a training sample and margin boundaries.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"71 1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83703392","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168165
Thi-Thu-Hong Phan, É. Poisson, A. Bigand, A. Lefebvre
Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper, we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of recurrent data. This method involves two main steps. Firstly, we find the most similar sub-sequence to the sub-sequence before (resp. after) a gap based on the shape-features extraction and Dynamic Time Warping algorithms. Secondly, we fill in the gap by the next (resp. previous) sub-sequence of the most similar one on the signal containing missing values. Experimental results show that our approach performs better than several related methods in case of multivariate time series having low/non-correlations and effective information on each signal.
{"title":"DTW-Approach for uncorrelated multivariate time series imputation","authors":"Thi-Thu-Hong Phan, É. Poisson, A. Bigand, A. Lefebvre","doi":"10.1109/MLSP.2017.8168165","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168165","url":null,"abstract":"Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper, we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of recurrent data. This method involves two main steps. Firstly, we find the most similar sub-sequence to the sub-sequence before (resp. after) a gap based on the shape-features extraction and Dynamic Time Warping algorithms. Secondly, we fill in the gap by the next (resp. previous) sub-sequence of the most similar one on the signal containing missing values. Experimental results show that our approach performs better than several related methods in case of multivariate time series having low/non-correlations and effective information on each signal.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85671399","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168156
Ryota Kawasumi, K. Takeda
We study the problem of matrix factorization by variational Bayes method, under the assumption that observed matrix is the product of low-rank dense and sparse matrices with additional noise. Under assumption of Laplace distribution for sparse matrix prior, we analytically derive an approximate solution of matrix factorization by minimizing Kullback-Leibler divergence between posterior and trial function. By evaluating our solution numerically, we also discuss accuracy of matrix factorization of our analytical solution.
{"title":"Approximate method of variational Bayesian matrix factorization with sparse prior","authors":"Ryota Kawasumi, K. Takeda","doi":"10.1109/MLSP.2017.8168156","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168156","url":null,"abstract":"We study the problem of matrix factorization by variational Bayes method, under the assumption that observed matrix is the product of low-rank dense and sparse matrices with additional noise. Under assumption of Laplace distribution for sparse matrix prior, we analytically derive an approximate solution of matrix factorization by minimizing Kullback-Leibler divergence between posterior and trial function. By evaluating our solution numerically, we also discuss accuracy of matrix factorization of our analytical solution.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"18 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88919391","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168125
Shogo Seki, H. Kameoka, T. Toda, K. Takeda
While time-frequency masking is a powerful approach for speech enhancement in terms of signal recovery accuracy (e.g., signal-to-noise ratio), it can over-suppress and damage speech components, leading to limited performance of succeeding speech processing systems. To overcome this shortcoming, this paper proposes a method to restore missing components of time-frequency masked speech spectrograms based on direct estimation of a time domain signal. The proposed method allows us to take account of the local interdepen-dencies of the elements of the complex spectrogram derived from the redundancy of a time-frequency representation as well as the global structure of the magnitude spectrogram. The effectiveness of the proposed method is demonstrated through experimental evaluation, using spectrograms filtered with masks to enhance of noisy speech. Experimental results show that the proposed method significantly outperformed conventional methods, and has the potential to estimate both phase and magnitude spectra simultaneously and precisely.
{"title":"Missing component restoration for masked speech signals based on time-domain spectrogram factorization","authors":"Shogo Seki, H. Kameoka, T. Toda, K. Takeda","doi":"10.1109/MLSP.2017.8168125","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168125","url":null,"abstract":"While time-frequency masking is a powerful approach for speech enhancement in terms of signal recovery accuracy (e.g., signal-to-noise ratio), it can over-suppress and damage speech components, leading to limited performance of succeeding speech processing systems. To overcome this shortcoming, this paper proposes a method to restore missing components of time-frequency masked speech spectrograms based on direct estimation of a time domain signal. The proposed method allows us to take account of the local interdepen-dencies of the elements of the complex spectrogram derived from the redundancy of a time-frequency representation as well as the global structure of the magnitude spectrogram. The effectiveness of the proposed method is demonstrated through experimental evaluation, using spectrograms filtered with masks to enhance of noisy speech. Experimental results show that the proposed method significantly outperformed conventional methods, and has the potential to estimate both phase and magnitude spectra simultaneously and precisely.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"23 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90155060","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}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168177
Thee Chanyaswad, Mert Al, J. M. Chang, S. Kung
In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based on Differential Mutual Information (DMI) and incremental forward search. DMI serves as an effective metric for selecting kernels, as is theoretically supported by mutual information and Fisher's discriminant analysis. On the other hand, incremental forward search plays a role in removing redundancy among kernels. Finally, we illustrate the potential of the method via an application in privacy-aware classification, and show on three mobile-sensing datasets that selecting an effective set of kernels for mKDCA feature-maps can enhance the utility classification performance, while successfully preserve the data privacy. Specifically, the results show that the proposed DMI forward search method can perform better than the state-of-the-art, and, with much smaller computational cost, can perform as well as the optimal, yet computationally expensive, exhaustive search.
{"title":"Differential mutual information forward search for multi-kernel discriminant-component selection with an application to privacy-preserving classification","authors":"Thee Chanyaswad, Mert Al, J. M. Chang, S. Kung","doi":"10.1109/MLSP.2017.8168177","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168177","url":null,"abstract":"In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based on Differential Mutual Information (DMI) and incremental forward search. DMI serves as an effective metric for selecting kernels, as is theoretically supported by mutual information and Fisher's discriminant analysis. On the other hand, incremental forward search plays a role in removing redundancy among kernels. Finally, we illustrate the potential of the method via an application in privacy-aware classification, and show on three mobile-sensing datasets that selecting an effective set of kernels for mKDCA feature-maps can enhance the utility classification performance, while successfully preserve the data privacy. Specifically, the results show that the proposed DMI forward search method can perform better than the state-of-the-art, and, with much smaller computational cost, can perform as well as the optimal, yet computationally expensive, exhaustive search.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"23 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73191416","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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