Robust sparse component analysis based on a generalized Hough transform

Authors:
Fabian J. Theis;Pando Georgiev;Andrzej Cichocki
Affiliations:
Institute of Biophysics, University of Regensburg, Regensburg, Germany;ECECS Department and Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH;BSI RIKEN, Laboratory for Advanced Brain Signal Processing, Saitama, Japan and Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2007

Citing 12
Cited 1

Robust regression and outlier detection

Robust regression and outlier detection
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis

Neural Computation
Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
k-Plane Clustering

Journal of Global Optimization
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A new concept for separability problems in blind source separation

Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary

Neural Computation
On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms

Signal Processing - Sparse approximations in signal and image processing
Analysis of multicomponent LFM signals by a combined Wigner-Houghtransform

IEEE Transactions on Signal Processing
Sparse component analysis and blind source separation of underdetermined mixtures

IEEE Transactions on Neural Networks

A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

An algorithm called Hough SCA is presented for recovering the matrix A in x(t) = As(t), where x(t)is a multivariate observed signal, possibly is of lower dimension than the unknown sources s(t). They are assumed to be sparse in the sense that at every time instant t, s(t) has fewer nonzero elements than the dimension of x(t). The presented algorithm performs a global search for hyperplane clusters within the mixture space by gathering possible hyperplane parameters within a Hough accumulator tensor. This renders the algorithm immune to the many local minima typically exhibited by the corresponding cost function. In contrast to previous approaches, Hough SCA is linear in the sample number and independent of the source dimension as well as robust against noise and outliers. Experiments demonstrate the flexibility of the proposed algorithm.