Can I read Generalized Independent Component Analysis Over Finite Alphabets on EtoBox?

Generalized Independent Component Analysis Over Finite Alphabets by Painsky, Amichai; Rosset, Saharon; Feder, Meir is a scholarly article available to read on EtoBox.

What is Generalized Independent Component Analysis Over Finite Alphabets about?

Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as possible from each other.Usually the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise). ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet. In this work we consider a generalization of this framework in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated. This generalization is also known as Barlow's minimal redundancy representation problem and is considered an open problem. We propose several theorems and show that this NP hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems. Our contribution provides the first efficient and constructive set of solutions to Barlow's problem.The minimal redundancy representation (also known as factorial code) has many appli

Author

Painsky, Amichai; Rosset, Saharon; Feder, Meir

Published

2015

Language

EN