One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this article, we propose a Maximum-Minimum Correntropy Criterion (MMCC) approach for se...

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science COST FUNCTIONS FOR SUPERVISED LEARNING BASED ON A ROBUST SIMILARITY METRIC By Abhishek Singh May 2010 Chair: José C. Prı́ncipe Major: Electrical and Computer Engineering This thesis proposes cost functions for supervised learning algorithms, based...

As one of the most critical issues for target track, α-jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α-jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the sta...

Constructing accurate similarity graph is an important process in graph-based clustering. However, traditional methods have three drawbacks, such as the inaccuracy of graph, vulnerability to noise and outliers, need for additional discretization process. In order eliminate these limitations, entropy regularized unsupervised clustering based on maximum correntropy criterion adaptive neighbors (E...

The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error ...