A new competitive learning algorithm for vector quantization

In this paper, a new competitive learning algorithm based on the partial distortion theorem is proposed for the on-line vector quantizer design. The novel algorithm is called partial-distortion-equivalent competitive learning (PDECL) algorithm, which aims at making the partial distortions for each neuron (code-vector) be uniform to overcome the neuron underuse problem as well as to minimize the average distortion for the designed vector quantizer. Compared with the Kohonen learning algorithm (KLA), the frequency-sensitive competitive learning (FSCL) algorithm and the soft competition scheme (SCS) algorithm, the PDECL consistently shows the better performance than all of them and the LBG algorithm for the design of vector quantizers with different codebook sizes especially when the codebook size is large enough.<<ETX>>

[1]  Stanley C. Ahalt,et al.  Neural Networks for Vector Quantization of Speech and Images , 1990, IEEE J. Sel. Areas Commun..

[2]  Allen Gersho,et al.  Asymptotically optimal block quantization , 1979, IEEE Trans. Inf. Theory.

[3]  Nasser M. Nasrabadi,et al.  Vector quantization of images based upon the Kohonen self-organizing feature maps , 1988, ICNN.

[4]  Fu-Lai Chung,et al.  Fuzzy competitive learning algorithm with decreasing fuzziness , 1993 .

[5]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[6]  Allen Gersho,et al.  Competitive learning and soft competition for vector quantizer design , 1992, IEEE Trans. Signal Process..

[7]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[8]  Nasser M. Nasrabadi,et al.  Vector quantization of images based upon the Kohonen self-organizing feature maps , 1988, IEEE 1988 International Conference on Neural Networks.

[9]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..