A fast algorithm for estimating overcomplete ICA bases for image windows

Author

Hyvärinen, Aapo ; Cristescu, Razvan ; Oja, Erkki

Author_Institution

Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland

Volume

2

fYear

1999

fDate

36342

Firstpage

894

Abstract

We introduce a very fast method for estimating over-complete bases of independent components from image data. This is based on the concept of quasi-orthogonality, which means that in a very high-dimensional space, there can be a large, over-complete set of vectors that are almost orthogonal to each other. Thus we may estimate an over-complete basis by using one-unit ICA algorithms and forcing only partial decorrelation between the different independent components. The method can be implemented using a modification of the FastICA algorithm, which leads to a computationally highly efficient method

Keywords

computational complexity; feature extraction; image recognition; maximum likelihood estimation; neural nets; optimisation; principal component analysis; FastICA algorithm; computational complexity; fast algorithm; heuristics; image data; image windows; independent component analysis; maximum likelihood estimation; over-complete set; partial decorrelation; quasi-orthogonality; Computational complexity; Decorrelation; Dictionaries; Feature extraction; Independent component analysis; Information science; Laboratories; Maximum likelihood estimation; Vectors; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831071

Filename

831071