Computer Science – Information Theory
Scientific paper
2005-09-28
Computer Science
Information Theory
10 pages, 2 figures, REVTEX preprint
Scientific paper
10.1016/j.physa.2006.01.013
The encoder and decoder for lossy data compression of binary memoryless sources are developed on the basis of a specific-type nonmonotonic perceptron. Statistical mechanical analysis indicates that the potential ability of the perceptron-based code saturates the theoretically achievable limit in most cases although exactly performing the compression is computationally difficult. To resolve this difficulty, we provide a computationally tractable approximation algorithm using belief propagation (BP), which is a current standard algorithm of probabilistic inference. Introducing several approximations and heuristics, the BP-based algorithm exhibits performance that is close to the achievable limit in a practical time scale in optimal cases.
Hosaka Tadaaki
Kabashima Yoshiyuki
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