Quantization of eigen subspace for sparse representation

Document Type

Article

Publication Date

7-15-2015

Abstract

We propose sparse Karhunen-Loeve Transform (SKLT) method to sparse eigen subspaces. The sparsity (cardinality reduction) is achieved through the pdf-optimized quantization of basis function (vector) set. It may be considered an extension of the simple and soft thresholding (ST) methods. The merit of the proposed framework for sparse representation is presented for auto-regressive order one, AR(1), discrete process and empirical correlation matrix of stock returns for NASDAQ-100 index. It is shown that SKLT is efficient to implement and outperforms several sparsity algorithms reported in the literature.

Identifier

84933556841 (Scopus)

Publication Title

IEEE Transactions on Signal Processing

External Full Text Location

https://doi.org/10.1109/TSP.2015.2430831

ISSN

1053587X

First Page

3616

Last Page

3625

Issue

14

Volume

63

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