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IEEE Final Year Project Topics for CSE

Base Paper Title

Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

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IEEE Project Abstract

Kernel-based clustering methods can capture the non-linear structure and identify arbitrarily shaped clusters, so they have been widely used in machine learning tasks. Since the performance of kernel methods critically depends on the choices of kernels, multiple kernel learning methods are proposed to alleviate the effort for kernel designing. Conventional multiple kernel learning methods learn a consensus kernel by linearly combining all candidate kernels, whereas ignore the influence of the noises. To improve the robustness of multiple kernel learning methods, in this paper, we analyse the local and global noises and design regularized terms to characterize them respectively. Then, we propose a novel local and global denoising multiple kernel learning method which can explicitly extract the local and global noises to recover the clean kernels for multiple kernel learning. After that, a block coordinate descent algorithm is present to solve the optimization problem. Finally, extensive experiments on benchmark data sets will demonstrate the effectiveness of the proposed algorithm.

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