A method to sparse eigen subspace and eigenportfolios

Document Type

Conference Proceeding

Publication Date

9-14-2015

Abstract

A new method to sparse eigen subspaces by using the pdf-optimized zero-zone quantizers is proposed. It is called sparse Karhunen-Loeve Transform (SKLT). The performance of the proposed method is presented for sparse representation of eigenportfolios generated from empirical correlation matrix of stock returns in NASDAQ-100 index. Performance results show that the proposed SKLT outperforms the popular algorithms to sparse eigen subspaces reported earlier in the literature.

Identifier

84960539585 (Scopus)

ISBN

[9780982443866]

Publication Title

2015 18th International Conference on Information Fusion Fusion 2015

First Page

1899

Last Page

1905

This document is currently not available here.

Share

COinS