Quantization of Eigen Subspace for Sparse Representation
IEEE Transactions on Signal Processing, Vol. 63, No. 14, July 2015
10 Pages Posted: 1 Dec 2015
Date Written: July 15, 2015
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.
Keywords: Arcsine distribution, cardinality reduction, dimension reduction, eigen decomposition, Karhunen–Loeve Transform (KLT), Lloyd-Max quantizer, midtread (zero-zone) pdf-optimized quantizer, principal component analysis (PCA), sparse matrix, subspace methods, transform coding
JEL Classification: C6
Suggested Citation: Suggested Citation