DocumentCode :
1925398
Title :
A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features
Author :
Deodhare, Dipti ; Vidyasagar, M. ; Murty, M. Narasimha
Author_Institution :
Centre for AI & Robotics, Bangalore
fYear :
2007
fDate :
5-7 March 2007
Firstpage :
348
Lastpage :
354
Abstract :
In general the objective of accurately encoding the input data and the objective of extracting good features to facilitate classification are not consistent with each other. As a result, good encoding methods may not be effective mechanisms for classification. In this paper, an earlier proposed unsupervised feature extraction mechanism for pattern classification has been extended to obtain an invertible map. The method of bimodal projection-based features was inspired by the general class of methods called projection pursuit. The principle of projection pursuit concentrates on projections that discriminate between clusters and not faithful representations. The basic feature map obtained by the method of bimodal projections has been extended to overcome this. The extended feature map is an embedding of the input space in the feature space. As a result, the inverse map exists and hence the representation of the input space in the feature space is exact. This map can be naturally expressed as a feedforward neural network
Keywords :
feature extraction; feedforward neural nets; pattern classification; bimodal projection-based feature; data encoding; feature map; feedforward neural network; pattern classification; projection pursuit method; unsupervised feature extraction; Artificial intelligence; Computer science; Data mining; Encoding; Feature extraction; Feedforward neural networks; Neural networks; Pattern classification; Robotics and automation; Scattering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing: Theory and Applications, 2007. ICCTA '07. International Conference on
Conference_Location :
Kolkata
Print_ISBN :
0-7695-2770-1
Type :
conf
DOI :
10.1109/ICCTA.2007.20
Filename :
4127394
Link To Document :
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