Branching Competitive Learning Network : A Novel Self-Creating Model  

Oleh:

Xiong, Huilin ; Swamy, M. N. S. ; Ahmad, M.O. ; King, Irwin

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 2 (Mar. 2004), page 417-429.
Topik: branching; branching; competitive learning; network; novel - self; creating mode

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper presents a new self - creating model of a neural network in which a branching mechanism is incorporated with competitive learning. Unlike other self - creating models, the proposed scheme, called branching competitive learning (BCL), adopts a special node -splitting criterion, which is based mainly on the geometrical measurements of the movement of the synaptic vectors in the weight space. Compared with other self - creating and non self - creating competitive learning models, the BCL network is more efficient to capture the spatial distribution of the input data and, therefore, tends to give better clustering or quantization results. We demonstrate the ability of the BCL model to appropriately estimate the cluster number in a data distribution, show its adaptability to non stationary data inputs and, moreover, present a scheme leading to a multiresolution data clustering. Extensive experiments on vector quantization of image compression are given to illustrate the effectiveness of the BCL algorithm.

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