Name
Title | ||
|---|---|---|
A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features |
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 |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICCTA.2007.20 | ICCTA |
Keywords | Field | DocType |
projection pursuit method,bimodal projection-based features,extended feature map,pattern classification,unsupervised feature extraction,inverse map,feedforward neural nets,unified approach,data encoding,invertible map,feature extraction,projection pursuit,feedforward neural network,basic feature,proposed unsupervised feature extraction,feature space,input space,bimodal projection-based feature,good feature,feature map,data mining,neural networks,encoding,feedforward neural networks,artificial intelligence,computer science,scattering | Computer vision,Feature vector,Feedforward neural network,Embedding,Projection pursuit,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Invertible matrix,Artificial neural network,Encoding (memory) | Conference |
ISBN | Citations | PageRank |
0-7695-2770-1 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dipti Deodhare | 1 | 18 | 5.14 |
| M. Vidyasagar | 2 | 122 | 32.78 |
| M. Narasimha Murty | 3 | 824 | 86.07 |