KNN Classification with One-step Computation
This work aims to improve the efficiency of KNN classification for practitioners by replacing its computationally intensive 'lazy part' with a more streamlined one-step computation.
The paper addresses the computational bottleneck of K-Nearest Neighbors (KNN) classification, specifically the lazy part involving setting K and searching for neighbors. It proposes a one-step computation that transforms this process into a matrix computation using least squares, a relationship matrix, and group lasso for sparse learning.
KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising