Sparse Component Analysis: a New Tool for Data Mining

Pando Georgiev (), Fabian Theis (), Andrzej Cichocki and Hovagim Bakardjian ()
Additional contact information
Pando Georgiev: University of Cincinnati
Fabian Theis: University of Regensburg
Hovagim Bakardjian: RIKEN

A chapter in Data Mining in Biomedicine, 2007, pp 91-116 from Springer

Abstract: Abstract In many practical problems for data mining the data X under consideration (given as (m × N)-matrix) is of the form X = AS, where the matrices A and S with dimensions m×n and n × N respectively (often called mixing matrix or dictionary and source matrix) are unknown (m ≤ n

Keywords: Sparse Component Analysis; Blind Signal Separation; clustering (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_6

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