Detecting and analyzing dense groups or communities from social and information networks
has attracted immense attention over the last decade due to its enormous applicability in …

Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …

Clustering is a fundamental problem in many data-driven application domains, and
clustering performance highly depends on the quality of data representation. Hence, linear …

Hydrometric information constitutes the fundamental input for planning, design, operation,
and management of water resources systems. How to optimally site monitoring gauges such …

Information theoretic based measures form a fundamental class of similarity measures for
comparing clusterings, beside the class of pair-counting based and set-matching based …

Ming Li Paul Vitányi Fourth Edition Page 1 An Introduction to Kolmogorov Complexity and Its
Applications Ming Li Paul Vitányi Fourth Edition Texts in Computer Science Page 2 Texts in …

Background Co-expression measures are often used to define networks among genes.
Mutual information (MI) is often used as a generalized correlation measure. It is not clear …

We present a new method for clustering based on compression. The method does not use
subject-specific features or background knowledge, and works as follows: First, we …

We discuss some methods to quantitatively investigate the properties of correlation matrices.
Correlation matrices play an important role in portfolio optimization and in several other …

Learning disentangled representations of natural language is essential for many NLP tasks,
eg, conditional text generation, style transfer, personalized dialogue systems, etc. Similar …