Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
A fast fixed-point algorithm for independent component analysis
Neural Computation
Learning nonlinear overcomplete representations for efficient coding
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
IEEE Transactions on Pattern Analysis and Machine Intelligence
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Stability and Chaos of a Class of Learning Algorithms for ICA Neural Networks
Neural Processing Letters
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We describe an application of independent component analysis (ICA) to pattern recognition in order to evaluate the effectiveness of features extracted by ICA. We propose a recognition method suitable for independent components that consists of modules for each category. A module has two parts: feature extraction and classification. Features are independent components estimated by ICA and outputs of modules are candidates for categories. These candidates are combined and categories are decided with a majority rule. This recognition method is applied to two tasks: hand-written digits in the MNIST database and acoustic diagnosis for a compressor as real-world tasks. A FastICA algorithm is applied to extracting independent features in the proposed method. Through recognition experiments, we demonstrate that the ICA of each category extracts useful features for these tasks and the independent components are superior to the principal components in recognition accuracy.