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Analysis of interactions among hidden components for Tucker model

Analysis of interactions among hidden components for Tucker ...

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Asia-Pacific signal and Information processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009

作者： Phan, Anh Huy Cichocki, Andrzej Lab for Advanced Brain Signal Processing BSI RIKEN Japan

Tensor representation and tensor decompositions are natural approaches to deal with large amounts of data with multiple aspects and high dimensionality in modern applications, such as environmental analysis, chemometrices, pharmaceutical analysis, spectral analysis, neuroscience. The two most popular decomposition/factorization models for N-th order tensors are the Tucker model and the more restricted PARAFAC model. The Tucker decomposition allows for the extraction of different numbers of factors in each of the modes, and permits interactions within each modality while PARAFAC does not. This advantage, however, is also one of the weakness of this decomposition. The difficult problem is to identify the dominant relationships between components, and to establish unique representation. In this paper, we will introduce a new measure index which is called the Joint Rate (JR) index, in order to evaluate interactions among various components in the general Tucker decomposition. The Hinton diagram is also extended to 3-D visualization. The use of the JR index will be illustrated with the analysis of EEG data for classification and BCI applications.

关键词： Spectrum analysis

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Classification of scenes based on multiway feature extraction

Classification of scenes based on multiway feature extractio...

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2010 International Conference on advanced Technologies for Communications, ATC 2010

作者： Phan, Anh Huy Cichocki, Andrzej Vu-Dinh, Thanh Lab for Advanced Brain Signal Processing Brain Science Institute RIKEN Japan Hochiminh City University of Technology Hochiminh City Viet Nam

ISBN: (纸本)9781424488766

Recognition of real world scenes can be efficiently solved based on global features termed the Spatial Envelope. Such features indeed comprise multiple modes such as orientations, scales, sparsity profiles. In order to extract features and classify multiway samples, most approaches vectorize data tensors to convert the classification of multiway data into the one of 1-D samples. This common approach disregards the multiway structures of global features, hence it can face the risk of losing correlation information between modes (orientations or scales). To this end, by revisiting the problem of scene classification in view of tensor decompositions, a new method is introduced to extract multiway features. The projection filter is designed for global features based on a set of basis matrices instead of only one basis as in 1-D problem. The proposed approach not only improves the classification accuracy, but also reduces the running time for training stage and feature projection. ©2010 IEEE.

关键词： Classification (of information)

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A tensorial approach to single trial recognition for brain computer interface

A tensorial approach to single trial recognition for brain c...

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International Conference on advanced Technologies for Communications, ATC

作者： Anh, Huy Phan Andrzej, Cichocki Thanh, Vu-Dinh Lab. for Advanced Brain Signal Processing Brain Science Institute RIKEN Japan Hochiminh City University of Technology Hochiminh City Viet Nam

ISBN: (纸本)9781424488766

In this paper, we propose a tensorial approach to single trial recognition in a EEG-based BCI system related to movement related potentials. In this approach input data are considered as tensors instead of more conventional vector or matrix representations. Feature extraction for multiway EEG spectral tensors is solved by using tensor (multi-array) decompositions. For the same EEG motor imagery dataset, the developed algorithms improved the accuracy of classification by almost 10% compared with the common spatial pattern (CSP) method. ©2010 IEEE.

关键词： Tensors

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Iterative projection approximation algorithms for PCA

Iterative projection approximation algorithms for PCA

引用

31st IEEE International Conference on Acoustics, Speech and signal processing

作者： Choi, Seungjin Ahn, Jong-Hoon Cichocki, Andrzej Department of Computer Science POSTECH South Korea Department of Physics POSTECH South Korea Advanced Brain Signal Processing Lab. BSI RIKEN Japan

ISBN: (纸本)9781424404681

In this paper we introduce a new error measure, integrated reconstruction error (IRE), the minimization of which leads to principal eigenvectors (without rotational ambiguity) of the data covariance matrix. Then we present iterative algorithms for the IRE minimization, through the projection approximation. The proposed algorithm is referred to as COnstrained Projection Approximation (COPA) algorithm and its limiting case is called COPAL. We also discuss regularized algorithms, referred to as R-COPA and R-COPAL. Numerical experiments demonstrate that these algorithms successfully find exact principal eigenvectors of the data covariance matrix.

关键词： Algorithms

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Common components analysis via linked blind source separation 40

Common components analysis via linked blind source separatio...

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40th IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2015

作者： Zhou, Guoxu Cichocki, Andrzej Mandic, Danilo P. Lab for Advanced Brain Signal Processing RIKEN Brain Science Institute Wako-shi Saitama Japan Department of Electrical and Electronic Engineering Imperial College London United Kingdom

ISBN: (纸本)9781467369978

Very often data we encounter in practice is a collection of matrices rather than a single matrix. These multi-block data often share some common features, due to the background in which they are measured. In this study we propose a new concept of linked blind source separation (BSS) that aims at discovering and extracting unique and physically meaningful common components from multi-block data, which also contain strong individual components. The validity and potential of the proposed method is justified by simulations. © 2015 IEEE.

关键词： group independent component analysis Linked blind source separation nonnegative matrix factorization

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Parameter Estimation of Maneuvering Target Using Maximum Likelihood Estimation for MIMO Radar with Colocated Antennas

Parameter Estimation of Maneuvering Target Using Maximum Lik...

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Workshop 3 2017

作者： Hai Li Ling Xu Zhe Zhang Tianjin Key Lab.for Advanced Signal Processing Civil Aviation University of China

The estimation of target parameters in MIMO radar signal processing is one of the most important research topics. An efficient implementation of the Maximum Likelihood estimator is presented in this paper to estimate the DOA(Direction of Arrival), initial velocity and acceleration of a maneuvering target in colocated MIMO radar. The target's DOA is estimated in the first place, then a Maximum-Likelihood(ML) estimation based on peak search is applied to a two-dimensional grids providing estimation of initial velocity and acceleration. Simulations show that the MIMO radar has a better performance in DOA estimation than the phased array radar. By means of Monte Carlo simulations, the estimation error of initial velocity and acceleration on different SNRs are calculated. The results also suggest the effectiveness of this method.

关键词： MIMO Radar Parameter Estimation Maximum Likelihood Estimation

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Multiway array decomposition analysis for classification of Alzheimer's disease

Multiway array decomposition analysis for classification of ...

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作者： Latchoumane, Charles-Francois Vincent Vialatte, Francois-Benois Cichocki, Andrzej Jeong, Jaeseung Department of Bio and Brain Engineering KAIST Daejeon 305-701 Korea Republic of Lab for Advanced Brain Signal Processing Brain Science Institute RIKEN 351-0198 Japan

As clinical investigation on Alzheimer's disease (AD) based on large patient population has been increased, generalized methods to detect diagnostic features are required for physiological datasets recorded in different sites (i.e. different hospitals). This work aimed at using the multiway array decomposition (MAD) method to extract discriminative features from electroencephalograms (EEGs) in Alzheimer's disease (AD). We applied modified versions of three MAD methods (i.e. the parallel factor analysis (PARAFAC), Tucker3 model, and nonnegative tensor decomposition (NTD)) to multi-site recorded EEGs in AD and age- and sex-matched healthy subjects. Feed-forward multilayer Perceptron was used and trained to validate and optimize for classification of AD using two independent EEG databases. We showed, using another independent EEG dataset, that the MAD approach exhibited larger than 90% of classification accuracy for AD, which outperformed supervised spectral-spatial filters or other previous conventional EEG analyses.

关键词： Electroencephalography

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Stereo Video Coding Based on Joint Motion/Disparity Estimation of Series Delaunay Triangulation Mesh

Stereo Video Coding Based on Joint Motion/Disparity Estimati...

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2012 International Conference on Convergence Computer Technology(IC3T 2012)

作者： Jiao Weidong Li Longshun Shen Xiaoyun Wan Di Tianjin Key Lab.for Advanced Signal Processing Civil Aviation Univ.of China

Compression of stereoscopic and multiview video data becomes more and more important since the bandwidth necessary for storage and transmission linearly increases with the number of camera channels.A new stereo video coding scheme based on both the joint motion/disparity estimation method of series Delaunay Triangulation mesh and the mesh object depiction of MPEG4 *** the data structure of streams is *** results show that the coder can compress the stereo video very efficiently while maintaining excellent visual perception quality.

关键词： Stereo video coding,Delaunay Triangulation mesh,Joint motion/Disparity estimation

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Novel alternating least squares algorithm for nonnegative matrix and tensor factorizations

Novel alternating least squares algorithm for nonnegative ma...

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17th International Conference on Neural Information processing, ICONIP 2010

作者： Phan, Anh Huy Cichocki, Andrzej Zdunek, Rafal Dinh, Thanh Vu Lab for Advanced Brain Signal Processing Brain Science Institute RIKEN Wako-shiSaitama351-0198 Japan Institute of Telecommunications Teleinformatics and Acoustics Wroclaw Poland HoChiMinh City University of Technology Viet Nam

ISBN: (纸本)9783642175367

Alternative least squares (ALS) algorithm is considered as a "work-horse" algorithm for general tensor factorizations. For nonnegative tensor factorizations (NTF), we usually use a nonlinear projection (rectifier) to remove negative entries during the iteration process. However, this kind of ALS algorithm often fails and cannot converge to the desired solution. In this paper, we proposed a novel algorithm for NTF by recursively solving nonnegative quadratic programming problems. The validity and high performance of the proposed algorithm has been confirmed for difficult benchmarks, and also in an application of object classification. © Springer-Verlag Berlin Heidelberg 2010.

关键词： Iterative methods

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Measures of goodness of fit to convolution model for analysis of FMRI data

Measures of goodness of fit to convolution model for analysi...

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17th European signal processing Conference, EUSIPCO 2009

作者： Nakamura, Wakako Inouye, Yujiro Faculty of Science and Engineering Shimane University Nishikawatsu 1060 Matsue Shimane 6908504 Japan Lab. for Advanced Brain Signal Processing Brain Science Institute RIKEN Hirosawa 2-1 Wako 3510198 Saitama Japan

On an fMRI data analysis, it is common to assume that we know when stimuli were presented or when subjects performed a task. However, for mental tasks such as memory retrieval, we cannot obtain an exact time of the task execution. When we use complex stimuli or natural stimuli such as a movie in experiments, then sometimes we cannot define the presentation time of stimuli straightforwardly. For these cases, we propose measures of a neural activity that we can obtain without a time series of stimuli presentations or task executions. We apply a blind deconvolution algorithm to an fMRI data set and separate it into a Hemodynamic Response Function (HRF) and a series of presentation times of stimuli. We propose to use values of the cost function for this separation algorithm as measures of a neural activity. The cost function is consisted of two terms. One is an error term representing discrepancy from a conventional convolution model of fMRI. The other term represents statistical characteristics of the estimated stimuli presentation time series. © EURASIP, 2009.

关键词： Cost functions

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