Journal
PATTERN RECOGNITION LETTERS
Volume 109, Issue -, Pages 44-54Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2017.09.036
Keywords
kNN method; kNN prediction; Parameter computation
Categories
Funding
- China Key Research Program [2016YFB1000905]
- China 973 Program [2013CB329404]
- China 1000-Plan National Distinguished Professorship
- Nation Natural Science Foundation of China [61573270, 61672177, 61363009]
- National Association of public funds
- Guangxi Natural Science Foundation [2015GXNSFCB139011]
- Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents
- Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing
- Research Fund of Guangxi Key Lab of MIMS [16-A-01-01, 16-A-01-02]
- Guangxi Bagui Teams for Innovation and Research
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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.
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