Search results for "information theory"
showing 10 items of 664 documents
Relaxation for a Class of Control Systems with Unilateral Constraints
2019
We consider a nonlinear control system involving a maximal monotone map and with a priori feedback. We assume that the control constraint multifunction $U(t,x)$ is nonconvex valued and only lsc in the $x \in \mathbb{R}^{N}$ variable. Using the Q-regularization (in the sense of Cesari) of $U(t,\cdot )$, we introduce a relaxed system. We show that this relaxation process is admissible.
Dimensionality reduction via regression on hyperspectral infrared sounding data
2014
This paper introduces a new method for dimensionality reduction via regression (DRR). The method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. These properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular…
A Feature Set Decomposition Method for the Construction of Multi-classifier Systems Trained with High-Dimensional Data
2013
Data mining for the discovery of novel, useful patterns, encounters obstacles when dealing with high-dimensional datasets, which have been documented as the "curse" of dimensionality. A strategy to deal with this issue is the decomposition of the input feature set to build a multi-classifier system. Standalone decomposition methods are rare and generally based on random selection. We propose a decomposition method which uses information theory tools to arrange input features into uncorrelated and relevant subsets. Experimental results show how this approach significantly outperforms three baseline decomposition methods, in terms of classification accuracy.
Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality
2015
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…
An empirical test of Sokolov's entropy model of the orienting response.
1974
Several hypotheses, most of them deduced from Sokolov's entropy model of the Orienting Response (OR), were tested. The Galvanic Skin Response (GSR) served as the indicator of the OR. Printed language, analyzed with regard to the information content in bits, was used as stimulus material. Forty-eight female students served as subjects. The results indicate: (1) that the uncertainty of a situation does not determine the strength of the OR, (2) that the strength of the OR depends on the information carried by an event, and (3) that the processing of this information, as indicated by the OR, may be delayed by one or more events in a serial application. For tonic level over a series of events no…
The meaning of biological signals.
2020
We introduce the virtual special issue on content in signalling systems. The issue explores the uses and limits of ideas from evolutionary game theory and information theory for explaining the content of biological signals. We explain the basic idea of the Lewis-Skyrms sender-receiver framework, and we highlight three key themes of the issue: (i) the challenge of accounting for deception, misinformation and false content, (ii) the relevance of partial or total common interest to the evolution of meaningful signals, and (iii) how the sender-receiver framework relates to teleosemantics.
Shallow Reductionism and the Problem of Complexity in Psychology
2008
In his recent book The Mind Doesn't Work That Way, Fodor argues that computational modeling of global cognitive processes, such as abductive everyday reasoning, has not been successful. In this article the problem is analyzed in the framework of algorithmic information theory. It is argued that the failed approaches are characterized by shallow reductionism, which is rejected in favor of deep reductionism and nonreductionism.
Multi-channel chromatic transformations for nonlinear color pattern recognition
2002
We present a new approach for color pattern recognition based on multi-channel nonlinear correlations. High discrimination capability is obtained in comparison with common linear multi-channel detection methods. We apply the nonlinear morphological correlation to different color channel decompositions as RGB and ATD channels. Moreover, in order to improve the discrimination we have introduced a new color transformation. When a high selectivity is required, the combination of the nonlinear correlation and the new color decomposition yields to detect the object using just a single channel. Simulation results are provided.
Explicit Upper Bound for Entropy Numbers
2004
We give an explicit upper bound for the entropy numbers of the embedding I : W r,p(Ql) → C(Ql) where Ql = (−l, l)m ⊂ Rm, r ∈ N, p ∈ (1,∞) and rp > m.
An algorithm for the solution of tree equations
1997
We consider the problem of solving equations over k-ary trees. Here an equation is a pair of labeled α-ary trees, where α is a function associating an arity to each label. A solution to an equation is a morphism from α-ary trees to k-ary trees that maps the left and right hand side of the equation to the same k-ary tree.