Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

Jiaye Li (Guangxi Normal University, Guilin 541004,)
Guoqiu Wen (Guangxi Normal University, Guilin 541004,)
Jiangzhang Gan (Guangxi Normal University, Guilin 541004,)
Leyuan Zhang (Guangxi Normal University, Guilin 541004,)
Shanwen Zhang (Guangxi Normal University, Guilin 541004,)

Emerging Science Journal
Vol 3, No 2 (2019): April

In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm.