ICA Model of Densities for Images (ICADGGDD)
Dheeraj Tandon1, Harsh Dev2, Deepak Kumar Singh3
1Dheeraj Tandon, SHIMT (Sitapur), Uttar Pradesh, India.
2Dr. Harsh Dev, PSIT (Kanpur), Uttar Pradesh, India.
3Dr. Deepak Kumar Singh, Integral University (Lucknow), Uttar Pradesh, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 5335-5339 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2969109119/2019©BEIESP | DOI: 10.35940/ijeat.A2969.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In data analysis we use ICA is a basic tool, the aim is that find out a co-ordinate system where are the components of the data are independent. Mostly ICA method such as fast ICA and Jointapproximation and diogonalization of eigen matrix (JADE), uses kurtosis as a metric of non gaussianity. But the assumption of kurtosis (fourth order cumulant) may not always satisfies practically. So there are one possible solution is to use skewness (third order cumulant) instead of kurtosis. In this paper we are going to introduce ICA based method, that approach is good for heavy-tailed (fourth order kurtosis) as well as asymmetric data (third order skewness).
Keywords: ICA, Skewness, Kurtosis.