Self-Organizing Maps, Vector Quantization, and Mixture Modeling  

Oleh:

Heskes, T.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 6 (2001), page 1299-1305.
Topik: Finite mixture models; self - organizing maps; vector quantization; mixture modeling

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Self - organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive expectation - maximization (EM) algorithms for self - organizing maps with and without missing values. We compare self - organizing maps with the elastic - net approach and explain why the former is better suited for the visualization of high - dimensional data. Several extensions and improvements are discussed. As an illustration we apply a self - organizing map based on a multinomial distribution to market basket analysis.

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