Abstract
In general, irrelevant features of high-dimensional data will degrade the performance of an inference system, e.g., a clustering algorithm or a classifier. In this paper, we therefore present a Local Kernel Regression (LKR) scoring approach to evaluate the relevancy of features based on their capabilities of keeping the local configuration in a small patch of data. Accordingly, a score index featuring applicability to both of supervised learning and unsupervised learning is developed to identify the relevant features within the framework of local kernel regression. Experimental results show the efficacy of the proposed approach in comparison with the existing methods.
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