We consider feature extraction (dimensionality reduction) for compositional data, where the data vectors are constrained to be positive and constant-sum. In real-world problems, t...
An important problem in many fields is the analysis of counts data to extract meaningful latent components. Methods like Probabilistic Latent Semantic Analysis (PLSA) and Latent ...
Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Sm...
: Semantic clustering is important to various fields in the modern information society. In this work we applied the Independent Component Analysis method to the extraction of the f...
"Learning with side-information" is attracting more and more attention in machine learning problems. In this paper, we propose a general iterative framework for relevant...
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unkno...