Pattern Classification Based on Sparse Representation

This paper presents a pattern classification method based on sparse representation. This new method bypasses the need for feature extraction and selection that are typically presented in the conventional classification methods, and performs classification using raw sensor signals directly. The performance of this new method is evaluated in the context of human physical activity assessment. Experimental results obtained from 105 human subjects demonstrate higher discriminative power than using the conventional k-nearest neighbor algorithm, verifying the effectiveness of the sparse representation method.