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A novel kNN algorithm with data-driven k parameter computation
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DOI：10.1016/j.patrec.2017.09.036.png)
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Abstract
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This paper studies an example-driven k-parameter computation that identifies different k values for different test samples in kNN prediction applications, such as classification, regression and missing data imputation. This is carried out with reconstructing a sparse coefficient matrix between test samples and training data. In the reconstruction process, an l(1)-norm regularization is employed to generate an element-wise sparsity coefficient matrix, and an LPP (Locality Preserving Projection) regularization is adopted to keep the local structures of data for achieving the efficiency. Further, with the learnt k value, k NN approach is applied to classification, regression and missing data imputation. We experimentally evaluate the proposed approach with 20 real datasets, and show that our algorithm is much better than previous k NN algorithms in terms of data mining tasks, such as classification, regression and missing value imputation. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
kNN method
kNN prediction
Parameter computation
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