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Title
Abstract
Introduction
Related Work
KNN Missing Data Imputation
Experimental Setting
Competing Methods
References
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Computer Science
Artificial Intelligence

Learning k for kNN Classification

Profile image of Debo ChengDebo Cheng

2017, ACM Transactions on Intelligent Systems and Technology

https://doi.org/10.1145/2990508
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19 pages

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Abstract

The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an ℓ 1 -norm regularizer and an ℓ 2, 1 -norm regularizer are applied to learn different k values for different test data and to result ...

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