Soft Learning Vector Quantization and Clustering Algorithms Based on Non-Euclidean Norms : Multinorm Algorithms  

Oleh:

Karayiannis, Nicolaos B. ; Randolph-Gips, Mary M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 1 (Jan. 2003), page 89-102.
Topik: algorithms; clustering; generator function; learning vector quantization (LVQ); non - euclidean norm; reformulation; reformulation function; weight matrix; weighted norm

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.8 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on multiple weighted norms to measure the distance between the feature vectors and their prototypes. Clustering and LVQ are formulated in this paper as the minimization of a reformulation function that employs distinct weighted norms to measure the distance between each of the prototypes and the feature vectors under a set of equality constraints imposed on the weight matrices. Fuzzy LVQ and clustering algorithms are obtained as special cases of the proposed formulation. The resulting clustering algorithm is evaluated and benchmarked on three data sets that differ in terms of the data structure and the dimensionality of the feature vectors. This experimental evaluation indicates that the proposed multinorm algorithm outperforms algorithms employing the Euclidean norm as well as existing clustering algorithms employing weighted norms.

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