Independent subspace analysis is unique, given irreducibility.
Lecture Notes Comp. Sci. 4666, 49-56 (2007)
Independent Subspace Analysis (ISA) is a generalization of ICA. It tries to find a basis in which a given random vector can be decomposed into groups of mutually independent random vectors. Since the first introduction of ISA, various algorithms to solve this problem have been introduced, however a general proof of the uniqueness of ISA decompositions remained an open question. In this contribution we address this question and sketch a proof for the separability of ISA. The key condition for separability is to require the subspaces to be not further decomposable (irreducible). Based on a decomposition into irreducible components, we formulate a general model for ISA without restrictions on the group sizes. The validity of the uniqueness result is illustrated on a toy example. Moreover, an extension of ISA to subspace extraction is introduced and its indeterminacies are discussed.
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 0302-9743
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 4666, Seiten: 49-56
Verlagsort Berlin [u.a.]
Begutachtungsstatus nicht peer-reviewed
Institut(e) Institute of Computational Biology (ICB)