Paper Info
Title
Hierarchical independent component analysis: A multi-resolution non-orthogonal data-driven basis.
Abstract
A new method named Hierarchical Independent Component Analysis is presented, particularly suited for dealing with two problems regarding the analysis of high-dimensional and complex data: dimensional reduction and multi-resolution analysis. It takes into account the Blind Source Separation framework, where the purpose is the research of a basis for a dimensional reduced space to represent data, whose basis elements represent physical features of the phenomenon under study. In this case orthogonal basis could be not suitable, since the orthogonality introduces an artificial constraint not related to the phenomenological properties of the analyzed problem. For this reason this new approach is introduced. It is obtained through the integration between Treelets and Independent Component Analysis, and it is able to provide a multi-scale non-orthogonal data-driven basis. Furthermore a strategy to perform dimensional reduction with a non orthogonal basis is presented and the theoretical properties of Hierarchical Independent Component Analysis are analyzed. Finally HICA algorithm is tested both on synthetic data and on a real dataset regarding electroencephalographic traces.
Year
DOI
Venue
2016
10.1016/j.csda.2015.09.014
Computational Statistics & Data Analysis
Keywords
DocType
Volume
Blind source separation,Dimensional reduction,Electroencephalography,Independent component analysis,Multi-resolution analysis,Treelets
Journal
95
ISSN
Citations 
PageRank 
0167-9473
2
0.48
References 
Authors
5
3
Name
Order
Citations
PageRank
Piercesare Secchi17011.12
Simone Vantini2609.26
Paolo Zanini320.82