Independent Component Analysis Using Convex Divergence
نویسندگان
چکیده
The convex divergence is used as a surrogate function for obtaining a class of ICA algorithms (Independent Component Analysis) called the f-ICA. The convex divergence is a super class of α-divergence, which is a further upper family of Kullback-Leibler divergence or mutual information. Therefore, the f-ICA contains the α-ICA and the minimum mutual information ICA. In addition to theoretical interest of generalization, the f-ICA contains a subset faster than the minimum mutual information ICA. It is found that this speed control is equivalent to the α-ICA. Finally, applications to brain fMRI map’s distillation is presented.
منابع مشابه
CONVEX DIVERGENCE AS A SURROGATE FUNCTION FOR INDEPENDENCE: THE f -DIVERGENCE ICA
The convex divergence is used as a surrogate function for obtaining independence of random variables described by the joint probability density. If the kernel convex function is twice continuously differentiable, this case reveals a class of generalized logarithm. This class of logarithms gives generalizations of the score function and the Fisher information matrix which are related to the Cram...
متن کاملINDEPENDENT COMPONENT ANALYSIS WITH JOINT SPEEDUP AND SUPERVISORY CONCEPT INJECTION: APPLICATIONS TO BRAIN fMRI MAP DISTILLATION
Methods to combine speedup terms and supervisory concept injection are presented. The speedup is based upon iterative optimization of the convex divergence. The injection of supervisory information is realized by adding a term which reduces an additional cost for a specified concept. Since the convex divergence includes usual logarithmic information measures, its direct application gives faster...
متن کاملConvex Divergence ICA
Independent component analysis (ICA) is vital for unsupervised learning and blind source separation (BSS). The ICA unsupervised learning procedure attempts to demix the observation vectors and identify the salient features or mixture sources. This work presents a novel contrast function for evaluating the dependence among sources. A convex divergence measure is developed by applying the convex ...
متن کاملA Convex Cauchy-Schwarz DivergenceMeasure for Blind Source Separation
Independent Component Analysis (ICA) for the demixing of multiple source mixtures. We call it the Convex Cauchy-Schwarz Divergence (CCS-DIV), and it is formed by integrating convex functions into the Cauchy-Schwarz inequality. The new measure is symmetric and the degree of its curvature with respect to the joint-distribution can be tuned by a (convexity) parameter. The CCS-DIV is able to speed-...
متن کاملConvex Cauchy Schwarz Independent Component Analysis for Blind Source Separation
—We present a new high-performance Convex Cauchy– Schwarz Divergence (CCS-DIV) measure for Independent Component Analysis (ICA) and Blind Source Separation (BSS). The CCS-DIV measure is developed by integrating convex functions into the Cauchy–Schwarz inequality. By including a convexity quality parameter, the measure has a broad control range of its convexity curvature. With this measure, a ne...
متن کامل