Towards theory of generic principal component analysis
Date
2009
Authors
Torokhti, A.
Friedland, S.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Journal of Multivariate Analysis, 2009; 100(4):661-669
Statement of Responsibility
Conference Name
Abstract
In this paper, we consider a technique called the generic Principal Component Analysis (PCA) which is based on an extension and rigorous justification of the standard PCA. The generic PCA is treated as the best weighted linear estimator of a given rank under the condition that the associated covariance matrix is singular. As a result, the generic PCA is constructed in terms of the pseudo-inverse matrices that imply a development of the special technique. In particular, we give a solution of the new low-rank matrix approximation problem that provides a basis for the generic PCA. Theoretical aspects of the generic PCA are carefully studied.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2008 Elsevier