Journal ArticleDOI
On the computation of rate-distortion functions (Corresp.)
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It is shown that the sequence of distributions used in that algorithm has a limit yielding a point on the R(d) curve if the reproducing alphabet is finite, and a similar but weaker result for countable reproducing alphabets is obtained.Abstract:
In a recent paper [1], Blahut suggested an efficient algorithm for computing rate-distortion functions. In this correspondence we show that the sequence of distributions used in that algorithm has a limit yielding a point on the R(d) curve if the reproducing alphabet is finite, and we obtain a similar but weaker result for countable reproducing alphabets.read more
Citations
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Proceedings ArticleDOI
Clustering with Bregman Divergences
TL;DR: This paper proposes and analyzes parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences, and shows that there is a bijection between regular exponential families and a largeclass of BRegman diverGences, that is called regular Breg man divergence.
Journal ArticleDOI
An algorithm for maximizing expected log investment return
TL;DR: An upper bound on W^{\ast} is given in terms of the current portfolio and the gradient, and the convergence of the algorithm is established.
Journal ArticleDOI
A mapping approach to rate-distortion computation and analysis
TL;DR: This paper reformulated the rate-distortion problem in terms of the optimal mapping from the unit interval with Lebesgue measure that would induce the desired reproduction probability density and shows how the number of "symbols" grows as the system undergoes phase transitions.
Journal ArticleDOI
Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle
TL;DR: This work applies the basic principle of this framework of bounded rational decision-making to perception-action systems with multiple information-processing nodes and derive bounded optimal solutions and formalizes a mathematically unifying optimization principle that could potentially be extended to more complex systems.
Book ChapterDOI
Alternating Minimization Algorithms: From Blahut-Arimoto to Expectation-Maximization
TL;DR: Two closely related algorithms in estimation theory are the expectation-maximization algorithm and the generalized iterative scaling algorithm, each of which can be written as alternating minimization algorithms.
References
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Journal ArticleDOI
Computation of channel capacity and rate-distortion functions
TL;DR: A simple algorithm for computing channel capacity is suggested that consists of a mapping from the set of channel input probability vectors into itself such that the sequence of probability vectors generated by successive applications of the mapping converges to the vector that achieves the capacity of the given channel.
Journal ArticleDOI
An algorithm for computing the capacity of arbitrary discrete memoryless channels
TL;DR: A systematic and iterative method of computing the capacity of arbitrary discrete memoryless channels is presented and a few inequalities that give upper and lower bounds on the capacity are derived.
Journal ArticleDOI
Progress in Picture Processing: 1969--71
TL;DR: Developments in the field of picture processing by computer during 1969-71 are surveyed and topics covered include picture compression, image enhancement, pictorial pattern recognition, scene analysis, and picture grammars.
Journal ArticleDOI
Bibliography on data compression, picture properties, and picture coding
L. Wilkins,P. Wintz +1 more
TL;DR: Published papers and reports dealing with data compression, picture properties and models, picture coding and transmission, image enhancement, and human visual information processing are listed.