Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646395
G. Nita, A. Keimpema, Z. Paragi
We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, after a 32-bit FFT operation is performed on the 2-bit time domain voltages, these two types of transients become distinguishable from each other in the spectral domain. Thus, we demonstrate the ability of the Spectral Kurtosis estimator to automatically detect bright astronomical transient signals of interests - such as pulsar or fast radio bursts (FRB) - in VLBI data streams that have been severely contaminated by unwanted radio frequency interference.
{"title":"STATISTICAL DETECTION AND CLASSIFICATION OF TRANSIENT SIGNALS IN LOW-BIT SAMPLING TIME-DOMAIN SIGNALS","authors":"G. Nita, A. Keimpema, Z. Paragi","doi":"10.1109/GLOBALSIP.2018.8646395","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646395","url":null,"abstract":"We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, after a 32-bit FFT operation is performed on the 2-bit time domain voltages, these two types of transients become distinguishable from each other in the spectral domain. Thus, we demonstrate the ability of the Spectral Kurtosis estimator to automatically detect bright astronomical transient signals of interests - such as pulsar or fast radio bursts (FRB) - in VLBI data streams that have been severely contaminated by unwanted radio frequency interference.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134147945","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646517
G. Cucho‐Padin, Yue Wang, L. Waldrop, Z. Tian, F. Kamalabadi
In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)’s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.
{"title":"EFFICIENT RFI DETECTION IN RADIO ASTRONOMY BASED ON COMPRESSIVE STATISTICAL SENSING","authors":"G. Cucho‐Padin, Yue Wang, L. Waldrop, Z. Tian, F. Kamalabadi","doi":"10.1109/GlobalSIP.2018.8646517","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646517","url":null,"abstract":"In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)’s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"38 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531131","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646581
Andrew Mackey, P. Spachos, K. Plataniotis
Indoor positioning systems are used in a variety of applications from shopping malls and museums to subject monitoring and tracking. The reliability and usability of such systems are highly based on their accuracy as well as cost and ease of deployment. Although the Global Positioning System (GPS) is an accurate solution for outdoor use, it can not be used indoors. A popular approach is a wireless navigation system which makes use of Received Signal Strength Indicators (RSSI). However, signal propagation, as well as surrounding noise and a dynamic environment, can affect their performance. Recent advancements in Bluetooth Low Energy (BLE) devices and the introduction of small and inexpensive beacons can alleviate the problem. In this work, we introduce an indoor navigation system with BLE beacons. To measure system accuracy an Android application was developed to collect the signal. Moreover, a Kalman filter was also developed within the application to improve the accuracy. Experimental results showed improvement of systems accuracy in three square topologies. The Kalman filter improved the accuracy up to 78.9%. while the experiments also show a correlation between the overall accuracy and how close BLE beacons are to each other.
{"title":"Enhanced Indoor Navigation System with Beacons and Kalman Filters","authors":"Andrew Mackey, P. Spachos, K. Plataniotis","doi":"10.1109/GlobalSIP.2018.8646581","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646581","url":null,"abstract":"Indoor positioning systems are used in a variety of applications from shopping malls and museums to subject monitoring and tracking. The reliability and usability of such systems are highly based on their accuracy as well as cost and ease of deployment. Although the Global Positioning System (GPS) is an accurate solution for outdoor use, it can not be used indoors. A popular approach is a wireless navigation system which makes use of Received Signal Strength Indicators (RSSI). However, signal propagation, as well as surrounding noise and a dynamic environment, can affect their performance. Recent advancements in Bluetooth Low Energy (BLE) devices and the introduction of small and inexpensive beacons can alleviate the problem. In this work, we introduce an indoor navigation system with BLE beacons. To measure system accuracy an Android application was developed to collect the signal. Moreover, a Kalman filter was also developed within the application to improve the accuracy. Experimental results showed improvement of systems accuracy in three square topologies. The Kalman filter improved the accuracy up to 78.9%. while the experiments also show a correlation between the overall accuracy and how close BLE beacons are to each other.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134535113","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646515
A. Kazemipour, S. Druckmann
In this paper, we introduce Poly-PCA, a nonlinear dimensionality reduction technique which can capture arbitrary nonlinearities in high-dimensional and dynamic data. Instead of optimizing over the space of nonlinear functions of high-dimensional data Poly-PCA models the data as nonlinear functions in the latent variables, leading to relatively fast optimization. Poly-PCA can handle nonlinearities which do not preserve the topology and geometry of the latents. Applying Poly-PCA to a nonlinear dynamical system successfully recovered the phase-space of the latent variables.
{"title":"Nonlinear Dimensionality Reduction Via Polynomial Principal Component Analysis","authors":"A. Kazemipour, S. Druckmann","doi":"10.1109/GlobalSIP.2018.8646515","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646515","url":null,"abstract":"In this paper, we introduce Poly-PCA, a nonlinear dimensionality reduction technique which can capture arbitrary nonlinearities in high-dimensional and dynamic data. Instead of optimizing over the space of nonlinear functions of high-dimensional data Poly-PCA models the data as nonlinear functions in the latent variables, leading to relatively fast optimization. Poly-PCA can handle nonlinearities which do not preserve the topology and geometry of the latents. Applying Poly-PCA to a nonlinear dynamical system successfully recovered the phase-space of the latent variables.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133843614","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646489
Daniyal Amir Awan, R. Cavalcante, Z. Utkovski, S. Stańczak
Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.
{"title":"SET-THEORETIC LEARNING FOR DETECTION IN CELL-LESS C-RAN SYSTEMS","authors":"Daniyal Amir Awan, R. Cavalcante, Z. Utkovski, S. Stańczak","doi":"10.1109/GLOBALSIP.2018.8646489","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646489","url":null,"abstract":"Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091200","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646650
Guobao Lu, Qilong Zhang, Xin Zhang, Fei Shen, F. Qin
Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects, especially the non-stationary temporal fading effect. Received Signal Strength Indicator (RSSI) will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power, while Rician K factor consisted by both the specular and scattered power can be treated as a reliable metric. The traditional estimation approaches of K factor from modulated wireless signals have to be data aided. In this paper, we attempt to formalize the estimation of K factor as a problem of non-linear feature extraction directly from modulated I/Q samples, which can be achieved through a simple convolutional neural network with morphological pre-processing. The experiments over field measurements have demonstrated the possibility of this methodology.
无线网络吸引了各行各业越来越多的兴趣。然而，由于严重的多径衰落效应，特别是非平稳的时间衰落效应，无线工业网络的广泛应用仍然面临着业务不可靠的挑战。接收信号强度指标(Received Signal Strength Indicator, RSSI)仅是对反射功率的噪声估计，在没有散射功率的情况下无法准确描述链路质量，而由反射功率和散射功率共同组成的rick因子可以作为可靠的度量。传统的无线调制信号K因子估计方法需要数据辅助。在本文中，我们试图将K因子的估计形式化为直接从调制I/Q样本中提取非线性特征的问题，这可以通过一个简单的卷积神经网络和形态学预处理来实现。现场测量实验证明了这种方法的可行性。
{"title":"CNN BASED RICIAN K FACTOR ESTIMATION FOR NON-STATIONARY INDUSTRIAL FADING CHANNEL","authors":"Guobao Lu, Qilong Zhang, Xin Zhang, Fei Shen, F. Qin","doi":"10.1109/GlobalSIP.2018.8646650","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646650","url":null,"abstract":"Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects, especially the non-stationary temporal fading effect. Received Signal Strength Indicator (RSSI) will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power, while Rician K factor consisted by both the specular and scattered power can be treated as a reliable metric. The traditional estimation approaches of K factor from modulated wireless signals have to be data aided. In this paper, we attempt to formalize the estimation of K factor as a problem of non-linear feature extraction directly from modulated I/Q samples, which can be achieved through a simple convolutional neural network with morphological pre-processing. The experiments over field measurements have demonstrated the possibility of this methodology.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165737","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646706
Marwan Alkhweldi, N. Schmid
This paper analyzes the potential of the Spatial Fourier transform (SFT) for detection of a periodic astrophysical signal and for estimation of parameters of the signal. In place of de-dispersing filter bank data for each Dispersion Measure (DM) trial and then integrating over frequency channels to yield a one-dimensional signal, we apply SFT to filter bank data, then detect periodic astrophysical signals and analyze their parameters such as DM and rotational period. This approach allows searching for periodic astrophysical signals in real time. Its complexity is dominated by the complexity of the SFT. The results of our analysis show promise. Using simulated data we demonstrate that it takes about 3 minutes of observation time to detect a pulsar at an S/N value of 8σ. The SFT data also provide information about the rotation of pulsars and lower and upper bounds on their DM value.
{"title":"SPATIAL FOURIER TRANSFORM FOR DETECTION AND ANALYSIS OF PERIODIC ASTROPHYSICAL PULSES","authors":"Marwan Alkhweldi, N. Schmid","doi":"10.1109/GlobalSIP.2018.8646706","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646706","url":null,"abstract":"This paper analyzes the potential of the Spatial Fourier transform (SFT) for detection of a periodic astrophysical signal and for estimation of parameters of the signal. In place of de-dispersing filter bank data for each Dispersion Measure (DM) trial and then integrating over frequency channels to yield a one-dimensional signal, we apply SFT to filter bank data, then detect periodic astrophysical signals and analyze their parameters such as DM and rotational period. This approach allows searching for periodic astrophysical signals in real time. Its complexity is dominated by the complexity of the SFT. The results of our analysis show promise. Using simulated data we demonstrate that it takes about 3 minutes of observation time to detect a pulsar at an S/N value of 8σ. The SFT data also provide information about the rotation of pulsars and lower and upper bounds on their DM value.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"71 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100661","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646444
Di Wang, Mengdi Huai, Jinhui Xu
In this paper, we present the first results on the sparse inverse covariance estimation problem under the differential privacy model. We first gave an ε-differentially private algorithm using output perturbation strategy, which is based on the sensitivity of the optimization problem and the Wishart mechanism. To further improve this result, we then introduce a general covariance perturbation method to achieve both ε-differential privacy and (ε, δ)-differential privacy. For ε-differential privacy, we analyze the performance of Laplacian and Wishart mechanisms, and for (ε, δ)-differential privacy, we examine the performance of Gaussian and Wishart mechanisms. Experiments on both synthetic and benchmark datasets confirm our theoretical analysis.
{"title":"DIFFERENTIALLY PRIVATE SPARSE INVERSE COVARIANCE ESTIMATION","authors":"Di Wang, Mengdi Huai, Jinhui Xu","doi":"10.1109/GlobalSIP.2018.8646444","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646444","url":null,"abstract":"In this paper, we present the first results on the sparse inverse covariance estimation problem under the differential privacy model. We first gave an ε-differentially private algorithm using output perturbation strategy, which is based on the sensitivity of the optimization problem and the Wishart mechanism. To further improve this result, we then introduce a general covariance perturbation method to achieve both ε-differential privacy and (ε, δ)-differential privacy. For ε-differential privacy, we analyze the performance of Laplacian and Wishart mechanisms, and for (ε, δ)-differential privacy, we examine the performance of Gaussian and Wishart mechanisms. Experiments on both synthetic and benchmark datasets confirm our theoretical analysis.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327912","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646465
Hamed Yazdanpanah, J. A. Apolinário, P. Diniz, Markus V. S. Lima
A class of algorithms known as feature least-mean-square (F-LMS) has been proposed recently to exploit hidden sparsity in adaptive filter parameters. In contrast to common sparsity-aware adaptive filtering algorithms, the F-LMS algorithm detects and exploits sparsity in linear combinations of filter coefficients. Indeed, by applying a feature matrix to the adaptive filter coefficients vector, the F-LMS algorithm can reveal and exploit their hidden sparsity. However, in many cases the unknown plant to be identified contains not only hidden but also plain sparsity and the F-LMS algorithm is unable to exploit it. Therefore, we can incorporate sparsity-promoting techniques into the F-LMS algorithm in order to allow the exploitation of plain sparsity. In this paper, by utilizing the l0-norm, we propose the l0-norm F-LMS (l0-F-LMS) algorithm for sparse lowpass and sparse highpass systems. Numerical results show that the proposed algorithm outperforms the F-LMS algorithm when dealing with hidden sparsity, particularly in highly sparse systems where the convergence rate is sped up significantly.
{"title":"l0-NORM FEATURE LMS ALGORITHMS","authors":"Hamed Yazdanpanah, J. A. Apolinário, P. Diniz, Markus V. S. Lima","doi":"10.1109/GlobalSIP.2018.8646465","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646465","url":null,"abstract":"A class of algorithms known as feature least-mean-square (F-LMS) has been proposed recently to exploit hidden sparsity in adaptive filter parameters. In contrast to common sparsity-aware adaptive filtering algorithms, the F-LMS algorithm detects and exploits sparsity in linear combinations of filter coefficients. Indeed, by applying a feature matrix to the adaptive filter coefficients vector, the F-LMS algorithm can reveal and exploit their hidden sparsity. However, in many cases the unknown plant to be identified contains not only hidden but also plain sparsity and the F-LMS algorithm is unable to exploit it. Therefore, we can incorporate sparsity-promoting techniques into the F-LMS algorithm in order to allow the exploitation of plain sparsity. In this paper, by utilizing the l0-norm, we propose the l0-norm F-LMS (l0-F-LMS) algorithm for sparse lowpass and sparse highpass systems. Numerical results show that the proposed algorithm outperforms the F-LMS algorithm when dealing with hidden sparsity, particularly in highly sparse systems where the convergence rate is sped up significantly.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124929140","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646691
A. Koochakzadeh, P. Pal
This paper considers canonical polyadic (CP) decomposition of symmetric even order tensors. In earlier work, we showed that decomposition of such tensors is equivalent to solving a system of quadratic equations. As part of ongoing work, we further show that for almost all tensors, singular value decomposition of a certain matrix can uniquely obtain the solution to the system of quadratic equations. Our proposed algorithm is able to find the CP-decomposition, even in the regime where the CP-rank exceeds the dimensions of the tensor (overcomplete tensors).
{"title":"Simplified Algorithms for Canonical Polyadic Decomposition for Over-Complete Even Order Tensors (Ongoing Work)","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/GlobalSIP.2018.8646691","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646691","url":null,"abstract":"This paper considers canonical polyadic (CP) decomposition of symmetric even order tensors. In earlier work, we showed that decomposition of such tensors is equivalent to solving a system of quadratic equations. As part of ongoing work, we further show that for almost all tensors, singular value decomposition of a certain matrix can uniquely obtain the solution to the system of quadratic equations. Our proposed algorithm is able to find the CP-decomposition, even in the regime where the CP-rank exceeds the dimensions of the tensor (overcomplete tensors).","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126172979","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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