A Universal Coding Scheme Based on Minimizing Minimax Redundancy for Sources with an Unknown Model

Joe SUZUKI

A Universal Coding Scheme Based on Minimizing Minimax Redundancy for Sources with an Unknown Model

Joe SUZUKI

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This paper's main objective is to clearly describe the construction of a universal code for minimizing Davisson's minimax redundancy in a range where the true model and stochastic parameters are unknown. Minimax redundancy is defined as the maximum difference between the expected persymbol code length and the per-symbol source entropy in the source range. A universal coding scheme is here formulated in terms of the weight function, i.e., a method is presented for determining a weight function which minimizes the minimax redundancy even when the true model is unknown. It is subsequently shown that the minimax redundancy achieved through the presented coding method is upper-bounded by the minimax redundancy of Rissanen's semi-predictive coding method.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.7 pp.1234-1239

Publication Date: 1993/07/25

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Type of Manuscript: PAPER

Category: Information Theory and Coding Theory

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Joe SUZUKI, "A Universal Coding Scheme Based on Minimizing Minimax Redundancy for Sources with an Unknown Model" in IEICE TRANSACTIONS on Fundamentals, vol. E76-A, no. 7, pp. 1234-1239, July 1993, doi: .
Abstract: This paper's main objective is to clearly describe the construction of a universal code for minimizing Davisson's minimax redundancy in a range where the true model and stochastic parameters are unknown. Minimax redundancy is defined as the maximum difference between the expected persymbol code length and the per-symbol source entropy in the source range. A universal coding scheme is here formulated in terms of the weight function, i.e., a method is presented for determining a weight function which minimizes the minimax redundancy even when the true model is unknown. It is subsequently shown that the minimax redundancy achieved through the presented coding method is upper-bounded by the minimax redundancy of Rissanen's semi-predictive coding method.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e76-a_7_1234/_p

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@ARTICLE{e76-a_7_1234,
author={Joe SUZUKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Universal Coding Scheme Based on Minimizing Minimax Redundancy for Sources with an Unknown Model},
year={1993},
volume={E76-A},
number={7},
pages={1234-1239},
abstract={This paper's main objective is to clearly describe the construction of a universal code for minimizing Davisson's minimax redundancy in a range where the true model and stochastic parameters are unknown. Minimax redundancy is defined as the maximum difference between the expected persymbol code length and the per-symbol source entropy in the source range. A universal coding scheme is here formulated in terms of the weight function, i.e., a method is presented for determining a weight function which minimizes the minimax redundancy even when the true model is unknown. It is subsequently shown that the minimax redundancy achieved through the presented coding method is upper-bounded by the minimax redundancy of Rissanen's semi-predictive coding method.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - A Universal Coding Scheme Based on Minimizing Minimax Redundancy for Sources with an Unknown Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1234
EP - 1239
AU - Joe SUZUKI
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 1993
AB - This paper's main objective is to clearly describe the construction of a universal code for minimizing Davisson's minimax redundancy in a range where the true model and stochastic parameters are unknown. Minimax redundancy is defined as the maximum difference between the expected persymbol code length and the per-symbol source entropy in the source range. A universal coding scheme is here formulated in terms of the weight function, i.e., a method is presented for determining a weight function which minimizes the minimax redundancy even when the true model is unknown. It is subsequently shown that the minimax redundancy achieved through the presented coding method is upper-bounded by the minimax redundancy of Rissanen's semi-predictive coding method.
ER -