Unsupervised feature extraction using neuro-fuzzy approach

Authors:
Rajat K. De;Jayanta Basak;Sankar K. Pal
Affiliations:
Machine Intelligence Unit, Indian Statistical Institute, 203, Barrackpore Trunk Road, Calcutta 700035, India;IBM India Research Lab, Block-I, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India and Machine Intelligence Unit, Indian Statistical Institute, Calcutta 700035, India;Machine Intelligence Unit, Indian Statistical Institute, 203, Barrackpore Trunk Road, Calcutta 700035, India
Venue:
Fuzzy Sets and Systems - Information processing
Year:
2002

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Abstract

The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction under unsupervised training. A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space that is obtained by a set of linear transformation on the original space. A layered network is designed for performing the task of minimization of the evaluation index through unsupervised learning process. This extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n' n) transformed space, along with their relative importance. The extracted features are found to provide better classification performance than the original ones for different real life data with dimensions 3, 4, 9, 18 and 34. The superiority of the method over principal component analysis network, nonlinear discriminant analysis network and Kohonen self-organizing feature map is also established.