A "nonnegative PCA" algorithm for independent component analysis

MD Plumbley; E Oja

doi:10.1109/TNN.2003.820672

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A "nonnegative PCA" algorithm for independent component analysis

Journal article

Peer reviewed

A "nonnegative PCA" algorithm for independent component analysis

MD Plumbley and E Oja

IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.15(1), pp.66-76

01/01/2004

DOI: https://doi.org/10.1109/TNN.2003.820672

Abstract

Science & Technology

Technology

Computer Science

Artificial Intelligence

Computer Science

Hardware & Architecture

Computer Science

Theory & Methods

Engineering

Electrical & Electronic

Computer Science

Engineering

independent component analysis (ICA)

nonlinear principal component analysis (nonlinear PCA)

nonnegative matrix factorization

subspace learning rule

MATRIX FACTORIZATION

SOURCE SEPARATION

CONSTRAINTS

CONVERGENCE

MODEL

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Title: A "nonnegative PCA" algorithm for independent component analysis
Creators: MD Plumbley
E Oja
Publication Details: IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.15(1), pp.66-76
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Date published: 01/01/2004
Date submitted: 17/05/2017
Identifiers: 99516866102346
Academic Unit: University of Surrey
Language: English
Resource Type: Journal article

A "nonnegative PCA" algorithm for independent component analysis

Abstract

Files and links (1)

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