Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality
reduction, has been in the ascendant since its inception. It incorporates the nonnegativity …

Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

This is the first comprehensive book on information geometry, written by the founder of the
field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide …

Fusing probabilistic information is a fundamental task in signal and data processing with
relevance to many fields of technology and science. In this work, we investigate the fusion of …

To quantify user–item preferences, a recommender system (RS) commonly adopts a high-
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …

Assessing the quality of natural language generation (NLG) systems through human
annotation is very expensive. Additionally, human annotation campaigns are time …

This paper addresses the estimation of the latent dimensionality in nonnegative matrix
factorization (NMF) with the (β)--divergence. The (β)-divergence is a family of cost functions …

Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS)
matrices filled with non-negative data, which are frequently encountered in industrial …

Source separation models that make use of nonnegativity in their parameters have been
gaining increasing popularity in the last few years, spawning a significant number of …

Large-scale undirected weighted networks are frequently encountered in big-data-related
applications concerning interactions among a large unique set of entities. Such a network …