| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560582 |
| ISSN: | 1405-5546 |
| Autores: | Pati, Rasmikanta1 Pujari, Arun K.2 Gahan, Padmavati3 Kumar, Vikas2 |
| Instituciones: | 1Sambalpur University Institute of Information Technology, India 2University Of Rajasthan, Rajasthan. India 3Sambalpur University Institute of Information Technology, Department of Business Administration, India 4University Of Hyderabad, Andhra Pradesh. India |
| Año: | 2021 |
| Periodo: | Ene-Mar |
| Volumen: | 25 |
| Número: | 1 |
| Paginación: | 97-115 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Independent Component Analysis (ICA) is an effective instrument for separating mixture signals from their blind sources that are specified or over-determined in the fields of signal processing, machine learning, data mining, finance, bio-medical, communications, artificial intelligence etc., ICA focuses primarily on finding an Objective Function (Contrast Function) and an appropriate optimization method to solve the problem. Different methods of ICA work out variously depending on how one models the contrast functions between themselves. ICA focuses mainly on finding components that are as independent as possible and as non-Gaussian as possible of an observed unexplained non-Gaussian Signal Mixture. ICA is an extremely important subject of great interest in numerous technological and scientific applications. In this article, we review a few different contrast functions in addition to the much earlier survey of Aapo Hyvarinen and widely used existing ICA algorithms in different scenarios for source separation. This article presents basic ideas on ICA, ICA algorithms and contrast functions. |
| Keyword: | Independent component analysis, Unsupervised learning, Particle swarm optimization, Higher order statistics, Blind source separation |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
