Name
Abstract | ||
|---|---|---|
Independent Component Analysis (ICA), a computationally efficient blind source separation technique,has been an area of interest for researchers for many practical applications in various fields of science and engineering. This paper attempts to cover the fundamental concepts involved in ICA techniques and review its applications. A thorough discussion of the applications and ambiguities problems of ICA has been carried out. Different ICA methods and their applications in various disciplines of science and engineering have been reviewed. In this paper, we present ICA methods from the basics to their potential applications to serve as a comprehensive single source for an inquisitive researcher to carry out research in this field. |
Year | Venue | Keywords |
|---|---|---|
2011 | INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS | independent component analysis, blind source separation, non-gaussianity, multi run ICA, overcomplete ICA, undercomplete ICA |
Field | DocType | Volume |
Computer science,Artificial intelligence,Independent component analysis,Blind signal separation,Machine learning,Area of interest | Journal | 35 |
Issue | ISSN | Citations |
1 | 0350-5596 | 30 |
PageRank | References | Authors |
1.32 | 35 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ganesh R. Naik | 1 | 298 | 25.37 |
| Dinesh Kant Kumar | 2 | 168 | 28.34 |