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

Oleh:

Karayiannis, N. B. ; Randolph-Gips, M. M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 2 (Mar. 2005), page 423-435.
Topik: algorithms; soft learning; vector quantization; clustering algorithm; non - euclidean norms; single - norm algorithms

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non - Euclidean algorithms which are computationally more demanding.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali