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Robust sparse component analysis based on a generalized Hough transform.
EURASIP J. Adv. Signal Process. 2007:052105 (2007)
An algorithm called Hough SCA is presented for recovering the matrix in , where is a multivariate observed signal, possibly is of lower dimension than the unknown sources . They are assumed to be sparse in the sense that at every time instant , has fewer nonzero elements than the dimension of . 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.
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
1110-8657
e-ISSN
1687-0433
Quellenangaben
Volume: 2007,
Article Number: 052105
Publisher
Springer
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)