Activity Recognition Method Based on Kernel Supervised Laplacian Eigenmaps

Abstract

Laplacian dimensionality reduction can effectively achieve feature transformation and preserve the important structure of high-dimensional features. However, the trained model with this method usually require better generalization ability to new samples. Hence, a human activity recognition method based on kernel-supervised laplacian eigenmaps (KSLE) by combining the kernel method, laplacian mapping, and supervised learning is proposed. Firstly, the adjacency distance relationship of original samples features in the high-dimensional kernel space is obtained. Secondly, the category labels in the high-dimensional feature set are incorporated in the manifold learning for dimensionality reduction. Then, kernel trick is utilized to directly solve the low-dimensional embedding structure of the test feature set. Finally, the obtained low-dimensional feature set is input into the classifier for recognition. Extensive experiments conduct on two public datasets confirm the effectiveness of the proposed approach for improving the generalization ability of new samples.