Independent component analysis:: algorithms and applications
Independent component analysis:: algorithms and applications
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DOI：
10.1016/s0893-6080(00)00026-5
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发表时间：
2000-05-01
期刊：
影响因子：
7.8
通讯作者：
Oja, E
中科院分区：
文献类型：
--
作者：
Hyvärinen, A;Oja, E
神经网络研究以及许多其他学科中的一个基本问题是找到多变量数据的合适表示，即随机向量。出于计算和概念简单性的原因，通常将表示作为原始数据的线性变换来寻求。换句话说，表示的每个分量都是原始变量的线性组合。公知的线性变换方法包括主成分分析、因子分析和投影寻踪。独立分量分析（伊卡）是近年来发展起来的一种新方法，其目标是寻找非高斯数据的线性表示，使各分量在统计上独立或尽可能独立。这种表示似乎在许多应用中捕获了数据的基本结构，包括特征提取和信号分离。本文介绍了伊卡的基本理论和应用，以及我们在这方面的最新工作。(C)2000年由Elsevier Science Ltd.出版
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so thar the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject. (C) 2000 Published by Elsevier Science Ltd.