Promise and Limitations of Supervised Optimal Transport-Based Graph Summarization via Information Theoretic Measures

Information Measures via Copula Functions

In applications of differential geometry to problems of parametric inference, the notion of divergence is often used to measure the separation between two parametric densities. Among them, in this paper, we will verify measures such as Kullback-Leibler information, J-divergence, Hellinger distance, -Divergence, … and so on. Properties and results related to distance between probability d...

Object recognition via information-theoretic measures/metrics

Graph Hybrid Summarization

One solution to process and analysis of massive graphs is summarization. Generating a high quality summary is the main challenge of graph summarization. In the aims of generating a summary with a better quality for a given attributed graph, both structural and attribute similarities must be considered. There are two measures named density and entropy to evaluate the quality of structural and at...

Multi-Document Summarization Using Graph-Based Iterative Ranking Algorithms and Information Theoretical Distortion Measures

Text summarization is an important field in the area of natural language processing and text mining. This paper proposes an extraction-based model which uses graphbased and information theoretic concepts for multidocument summarization. Our method constructs a directed weighted graph from the original text by adding a vertex for each sentence, and compute a weighted edge between sentences which...

Information-theoretic Semi-supervised Metric Learning via Entropy Regularization

We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For met...