Fuzzy competitive learning algorithm with decreasing fuzziness

Chung, Fu-Lai; Lee, T.

doi:10.1049/el:19930806

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…For this reason, various competitive learning algorithms gradually turn into the basic CL algorithm with time for the nearest neighbor partition. The soft competition algorithm gradually evolves into the basic CL learning as the temperature decreases with time [11], and the fuzzy CL algorithms change into the basic CL algorithm with decreasing fuzziness [13], [14]. The optimization techniques such as the stochastic relaxation with decreasing perturbation [6], [19] will also eventually evolve into the basic learning scheme based on the nearest neighbor partition and the centroid conditions.…”

Section: On-line Implementation Of Minimax Partial Distortion Critmentioning

confidence: 99%

“…Moreover, the frequency-sensitive competitive learning (FSCL) algorithm [9], [10] is a straightforward and simple implementation of the "conscience" scheme, in which each competing codevector assumes an approximately uniform win rate to thoroughly eliminate the underuse problem. Some other improved CL algorithms utilize the soft competition scheme [11], [12] or the fuzzy mechanism [13], [14] to adjust all the codevectors with different adaptation weights for each presentation of an input vector. However, determining the weights requires additional time-consuming computations such as power operations.…”

Section: Introductionmentioning

confidence: 99%

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Minimax partial distortion competitive learning for optimal codebook design

Zhu¹,

1998

IEEE Trans. on Image Process.

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The design of the optimal codebook for a given codebook size and input source is a challenging puzzle that remains to be solved. The key problem in optimal codebook design is how to construct a set of codevectors efficiently to minimize the average distortion. A minimax criterion of minimizing the maximum partial distortion is introduced in this paper. Based on the partial distortion theorem, it is shown that minimizing the maximum partial distortion and minimizing the average distortion will asymptotically have the same optimal solution corresponding to equal and minimal partial distortion. Motivated by the result, we incorporate the alternative minimax criterion into the on-line learning mechanism, and develop a new algorithm called minimax partial distortion competitive learning (MMPDCL) for optimal codebook design. A computation acceleration scheme for the MMPDCL algorithm is implemented using the partial distance search technique, thus significantly increasing its computational efficiency. Extensive experiments have demonstrated that compared with some well-known codebook design algorithms, the MMPDCL algorithm consistently produces the best codebooks with the smallest average distortions. As the codebook size increases, the performance gain becomes more significant using the MMPDCL algorithm. The robustness and computational efficiency of this new algorithm further highlight its advantages.

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