Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …
[图书][B] Independent component analysis
A Hyvärinen, J Hurri, PO Hoyer, A Hyvärinen, J Hurri… - 2009 - Springer
In this chapter, we discuss a statistical generative model called independent component
analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse …
analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse …
[引用][C] Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Cichocki - John Wiley & Sons google schola, 2002 - books.google.com
With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …
Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation
A Yeredor - IEEE Transactions on signal processing, 2002 - ieeexplore.ieee.org
Approximate joint diagonalization of a set of matrices is an essential tool in many blind
source separation (BSS) algorithms. A common measure of the attained diagonalization of …
source separation (BSS) algorithms. A common measure of the attained diagonalization of …
[PDF][PDF] A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation
A new efficient algorithm is presented for joint diagonalization of several matrices. The
algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem …
algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem …
Blind identification of underdetermined mixtures by simultaneous matrix diagonalization
L De Lathauwer, J Castaing - IEEE Transactions on Signal …, 2008 - ieeexplore.ieee.org
In this paper, we study simultaneous matrix diagonalization-based techniques for the
estimation of the mixing matrix in underdetermined independent component analysis (ICA) …
estimation of the mixing matrix in underdetermined independent component analysis (ICA) …
Blind identification of under-determined mixtures based on the characteristic function
P Comon, M Rajih - Signal Processing, 2006 - Elsevier
Linear mixtures of independent random variables (the so-called sources) are sometimes
referred to as under-determined mixtures (UDM) when the number of sources exceeds the …
referred to as under-determined mixtures (UDM) when the number of sources exceeds the …
Provable ICA with unknown Gaussian noise, with implications for Gaussian mixtures and autoencoders
We present a new algorithm for Independent Component Analysis (ICA) which has provable
performance guarantees. In particular, suppose we are given samples of the form $ y …
performance guarantees. In particular, suppose we are given samples of the form $ y …
Fourier PCA and robust tensor decomposition
Fourier PCA is Principal Component Analysis of a matrix obtained from higher order
derivatives of the logarithm of the Fourier transform of a distribution. To make this …
derivatives of the logarithm of the Fourier transform of a distribution. To make this …
[图书][B] Biomedical signal analysis: Contemporary methods and applications
FJ Theis, A Meyer-Bäse - 2010 - books.google.com
A comprehensive introduction to innovative methods in the field of biomedical signal
analysis, covering both theory and practice. Biomedical signal analysis has become one of …
analysis, covering both theory and practice. Biomedical signal analysis has become one of …