A method to sparse eigen subspace and eigenportfolios

Onur Yilmaz, Ali Akansu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2015 18th International Conference on Information Fusion, Fusion 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1899-1905
Number of pages7
ISBN (Electronic)9780982443866
StatePublished - Sep 14 2015
Event18th International Conference on Information Fusion, Fusion 2015 - Washington, United States
Duration: Jul 6 2015Jul 9 2015

Publication series

Name2015 18th International Conference on Information Fusion, Fusion 2015

Other

Other18th International Conference on Information Fusion, Fusion 2015
CountryUnited States
CityWashington
Period7/6/157/9/15

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing
  • Computer Networks and Communications

Keywords

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

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