kNN Regression Algorithm Based on LPP and Lasso
This paper proposed a newnearest neighbor algorithm based LPP and Lasso,for solving the fixed k value problem and correlation among the samples wasn' t considered of k NN algorithms. The proposed algorithm combined Locality Preserving Projections( LPP) with sparse coding( e.g.,Lasso) to restructure test samples with the training data.During the reconstruction process,LPP was used to preserve the local structures of the data and the l1-norm was used to learn different k value for various samples.Experiments results on UCI datasets showed that the proposed methods were superior to traditional k NN algorithm in terms of regression performance.