LOGSPLINE INDEPENDENT COMPONENT ANALYSIS
-
- Kawaguchi Atsushi
- Biostatistics Center, Kurume University
-
- Truong Young K.
- Department of Biostatistics, The University North Carolina at Chapel Hill
Search this article
Abstract
Most recent maximum likelihood approaches to independent component analysis (ICA) are based on nonparametric density estimation. In this paper, we present an algorithm by using the logsplines approach to density estimation. The logarithmic source density functions are modeled by polynomial splines or a linear combination of B-splines with (a) parameters or coefficients of the B-splines estimated by maximizing the log-likelihood function, and (b) knots of the B-splines determined by a stepwise procedure so as to minimize the approximation errors in modeling the log-density functions. We showed in a comparative study that our new algorithm has performed very favorably when compared to several popular density estimation based procedures.
Journal
-
- Bulletin of informatics and cybernetics
-
Bulletin of informatics and cybernetics 43 83-94, 2011-12
Research Association of Statistical Sciences
- Tweet
Keywords
Details
-
- CRID
- 1390290699825840256
-
- NII Article ID
- 120005397802
-
- NII Book ID
- AA10634475
-
- DOI
- 10.5109/1434313
-
- ISSN
- 2435743X
- 0286522X
-
- HANDLE
- 2324/1434313
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- IRDB
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Allowed