Quantization of eigen subspace for sparse representation

Onur Yilmaz, Ali Akansu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Article number7103342
Pages (from-to)3616-3625
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume63
Issue number14
DOIs
StatePublished - Jul 15 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Arcsine distribution
  • Karhunen-Loeve Transform (KLT)
  • Lloyd-Max quantizer
  • cardinality reduction
  • dimension reduction
  • eigen decomposition
  • midtread (zero-zone) pdf-optimized quantizer
  • principal component analysis (PCA)
  • sparse matrix
  • subspace methods
  • transform coding

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