Fast Self-Organizing Feature Map Algorithm  

Oleh:

Su, Mu-Chun ; Chang, Hsiao-Te

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 3 (2000), page 721-733.
Topik: FEATURE; self - organizing; feature map; algorithm

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K - means algorithm to select N2 (i. e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N × N neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine - tuned by the traditional Kohonen self - organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three - stage method, a topologically ordered feature map would be formed very quickly instead of requiring a huge amount of iterations to fine - tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm. Three data sets are utilized to illustrate the proposed method.

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