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Francisco J. Valverde-Albacete

Publications and source records attributed to Francisco J. Valverde-Albacete.

2 recordsLinked to original sources

Entropy operates in non-linear semifields

We set out to demonstrate that the Rényi entropies with parameter $α$ are better thought of as operating in a type of non-linear semiring called a positive semifield. We show how the Rényi's postulates lead to Pap's g-calculus where the functions carrying out the domain transformation are Renyi's information function and its inverse. In its turn, Pap's g-calculus under Rényi's information function transforms the set of positive reals into a family of semirings where "standard" product has been transformed into sum and "standard" sum into a power-deformed sum. Consequently, the transformed product has an inverse whence the structure is actually that of a positive semifield. Instances of this construction lead into idempotent analysis and tropical algebra as well as to less exotic structures. Furthermore, shifting the definition of the $α$ parameter shows in full the intimate relation of the Rényi entropies to the weighted generalized power means. We conjecture that this is one of the reasons why tropical algebra procedures, like the Viterbi algorithm of dynamic programming, morphological processing, or neural networks are so successful in computational intelligence applications. But also, why there seem to exist so many procedures to deal with "information" at large.

cs.IT↗

Assessing Information Transmission in Data Transformations with the Channel Multivariate Entropy Triangle

Data transformation, e.g. feature transformation and selection, is an integral part of any machine learning procedure. In this paper we introduce an information-theoretic model and tools to assess the quality of data transformations in machine learning tasks. In an unsupervised fashion, we analyze the transfer of information of the transformation of a discrete, multivariate source of information X into a discrete, multivariate sink of information Y related by a distribution PXY . The first contribution is a decomposition of the maximal potential entropy of (X, Y) that we call a balance equation, into its a) non-transferable, b) transferable but not transferred and c) transferred parts. Such balance equations can be represented in (de Finetti) entropy diagrams, our second set of contributions. The most important of these, the aggregate Channel Multivariate Entropy Triangle is a visual exploratory tool to assess the effectiveness of multivariate data transformations in transferring information from input to output variables. We also show how these decomposition and balance equation also apply to the entropies of X and Y respectively and generate entropy triangles for them. As an example, we present the application of these tools to the assessment of information transfer efficiency for PCA and ICA as unsupervised feature transformation and selection procedures in supervised classification tasks.

cs.IT↗