Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
A Mathematical Theory of Communication
A Mathematical Theory of Communication
One-to-All Broadcasting Scheme for Static Interconnection Networks with Arbitrary Topology
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Effective, design-independent XML keyword search
Proceedings of the 18th ACM conference on Information and knowledge management
Keyword search for data-centric XML collections with long text fields
Proceedings of the 13th International Conference on Extending Database Technology
Problems adopting metrics from other disciplines
Proceedings of the 2010 ICSE Workshop on Emerging Trends in Software Metrics
Using structural information in XML keyword search effectively
ACM Transactions on Database Systems (TODS)
Relational Graph Mining for Learning Events from Video
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Parameter-free anomaly detection for categorical data
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Two multivariate generalizations of pointwise mutual information
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
Multi-information ensemble diversity
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
A structure description of visual information
Pattern Recognition Letters
Entropy metrics for software design evaluation
Journal of Systems and Software
The evolution of representation in simple cognitive networks
Neural Computation
Synergy, redundancy, and multivariate information measures: an experimentalist's perspective
Journal of Computational Neuroscience
| Hi-index | 0.00 |
A set λ of stochastic variables, y1, y2,..., yn, is grouped into subsets, µ1, µ2,..., µk. The correlation existing in λ with respect to the µ's is adequately expressed by C= Σi=1k S(µi)-S(λ)≥0, where S(v) is the entropy function defined with reference to the variables y in subset v. For a given λ, C becomes maximum when each µi consists of only one variable, (n=k). The value Cis then called fhe total correlation in λ, Ctot(λ). The present paper gives various theorems, according to which Ctot(λ) can be decomposed in terms of the partial correlations existing in subsets of λ, and of quantities derivable therefrom. The information-theoretical meaning of each decomposition is carefully explained. As illustrations, two problems are discussed at the end of the paper: (1) redundancy in geometrical figures in pattern recognition, and (2) randomization effect of shuffling cards marked "zero' or "one."