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The past decade has seen the rapid development of information theoretic learning and its applications in signal processing and machine learning. Specifically, minimum error entropy (MEE) and maximum correntropy criterion (MCC) have been widely studied in the literature. Although MEE and MCC are applied in many branches of knowledge and could outperform statistical criteria (such as mean square error), they have not been compared with each other from theoretical point of view. In some cases, MEE and MCC perform similarly to each other; however, under some conditions (e.g., in non-Gaussian environments), they act differently. This letter derives a new information theoretic relation between MEE and MCC, leading to better understanding of the theoretical differences, and illustrates the findings in a common example. [ABSTRACT FROM AUTHOR]

This paper presents a new method for analyzing array comparative genomic hybridization (aCGH) data based on Correntropy. A new formulation based on low-rank aCGH data and Correntropy is proposed and its solution is presented based on Half-Quadratic method. Compared to existing methods, the proposed method is more robust to high corruptions and various kinds of noise. Moreover, it analyzes all aCGH profiles relating to a data set simultaneously. Experimental results illustrate the robustness of the proposed method when the noise is non-Gaussian and show its excellent performance in other cases. [ABSTRACT FROM AUTHOR]