Bareera Zafar1, Syed Abbas Zilqurnain Naqvi1, Muhammad Ahsan1, Allah Ditta2,*, Ummul Baneen1, Muhammad Adnan Khan3,4
CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.027874
Abstract This research proposes a method called enhanced collaborative and geometric multi-kernel learning (E-CGMKL) that can enhance the CGMKL algorithm which deals with multi-class classification problems with non-linear data distributions. CGMKL combines multiple kernel learning with softmax function using the framework of multi empirical kernel learning (MEKL) in which empirical kernel mapping (EKM) provides explicit feature construction in the high dimensional kernel space. CGMKL ensures the consistent output of samples across kernel spaces and minimizes the within-class distance to highlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them and try… More >
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