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Kernel K-means can handle nonlinearly separate datasets by mapping the input datasets into a high-dimensional feature space. The kernel matrix reflects the inner structure of data, so it is a key to construct an appropriate kernel matrix. However, many kernel-based methods need to be set kernel parameter artificially in advance. It is difficult to set an appropriate kernel parameter for each dataset...
Most of multiple kernel clustering algorithms aim to find the optimal kernel combination and have to calculate kernel weights iteratively. For the kernel methods, the scale parameter of Gaussian kernel is usually searched in a number of candidate values of the parameter and the best is selected. In this paper, a novel locally adaptive multiple kernel k-means algorithm is proposed based on shared nearest...
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