A novel kNN algorithm with data-driven k parameter computation.

This paper studies an example-driven k -parameter computation that identifies different k values for different test samples in k NN 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. [ABSTRACT FROM AUTHOR]