Feature analysis (FA) is an essential step in designing useful “intelligent systems”. In a broader sense, FA is a mapping Φ: Rp → Rq, which transforms a given data set X in Rp to a data set Y in Rq; usually q < p. Here Φ could be liner or nonlinear and identified using supervised or unsupervised methods. When designers want to get a visual representation of X, usually Φ extracts Y in R2 or R3. Such a representation is quite useful, but usually the extracted features are not interpretable and hence for critical applications, designer prefers dimensionality reduction via feature selection. Ideally a feature selection scheme should select necessary features, discard derogatory features and indifferent features, and it should maintain a controlled level of redundancy. Complete elimination of redundancy is not desirable as then the system may not be able to tolerate any measurement error. Sensor selection is a generalization of feature selection where a sensor is responsible for a set of features. In this talk, first I shall discuss how neural networks can be effectively used for structure-preserving dimensionality reduction. This approach does not use class labels and since it preserves the “inherent structure” in the high dimensional data, it is quite effective for most applications, in particular, for data visualization. This will be followed by how neural networks can be adapted to select useful features discarding derogatory and indifferent features. The neural model will then be enhanced to deal with the problem of sensor selection. Finally, I shall present how the neural networks can select features (as well as sensors) with a control on the level of redundancy in the set of selected features (sensors).
Nikhil R. Pal is a Professor in the Electronics and Communication Sciences Unit of the Indian Statistical Institute. His current research interest includes bioinformatics, brain science, fuzzy logic, pattern analysis, neural networks, and evolutionary computation.
He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems for the period January 2005-December 2010. He has served/been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, Neural Information Processing-Letters and Reviews, International Journal of Knowledge-Based Intelligent Engineering Systems, International Journal of Neural Systems, Fuzzy Sets and Systems, International Journal of Intelligent Computing in Medical Sciences and Image Processing, Fuzzy Information and Engineering: An International Journal, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems Man and Cybernetics-B.
He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He was a Distinguished Lecturer of the IEEE Computational Intelligence Society (CIS) and was a member of the Administrative Committee of the IEEE CIS. At present he is President Elect of the IEEE CIS. He is a Fellow of the National Academy of Sciences, India, a Fellow of the Indian National Academy of Engineering, a Fellow of the Indian National Science Academy, a Fellow of the International Fuzzy Systems Association (IFSA), and a Fellow of the IEEE, USA. He received the IEEE CIS Fuzzy Pioneer award.