On Self-Organizing Algorithms and Networks for Class-Separability Features  

Oleh:

Chatterjee, C. ; Roychowdhury, V. P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 3 (1997), page 663-678.
Topik: grammatical features; self - organizing; algorithm; network; class - separability; features

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We describe self - organizing learning algorithms and associated neural networks to extract features that are effective for preserving class separability. As a first step, an adaptive algorithm for the computation of Q - 1/2 (where Q is the correlation or covariance matrix of a random vector sequence) is described. Convergence of this algorithm with probability one is proven by using stochastic approximation theory, and a single-layer linear network architecture for this algorithm is described, which we call the Q - 1/2 network. Using this network, we describe feature extraction architectures for : 1) unimodal and multicluster Gaussian data in the multiclass case ; 2) multivariate linear discriminant analysis (LDA) in the multiclass case ; and 3) Bhattacharyya distance measure for the two - class case. The LDA and Bhattacharyya distance features are extracted by concatenating the Q -1/2 network with a principal component analysis network, and the two-layer network is proven to converge with probability one. Every network discussed in the study considers a flow or sequence of inputs for training. Numerical studies on the performance of the networks for multiclass random data are presented.

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