1 research outputs found
Robust Classification with Sparse Representation Fusion on Diverse Data Subsets
Sparse Representation (SR) techniques encode the test samples into a sparse
linear combination of all training samples and then classify the test samples
into the class with the minimum residual. The classification of SR techniques
depends on the representation capability on the test samples. However, most of
these models view the representation problem of the test samples as a
deterministic problem, ignoring the uncertainty of the representation. The
uncertainty is caused by two factors, random noise in the samples and the
intrinsic randomness of the sample set, which means that if we capture a group
of samples, the obtained set of samples will be different in different
conditions. In this paper, we propose a novel method based upon Collaborative
Representation that is a special instance of SR and has closed-form solution.
It performs Sparse Representation Fusion based on the Diverse Subset of
training samples (SRFDS), which reduces the impact of randomness of the sample
set and enhances the robustness of classification results. The proposed method
is suitable for multiple types of data and has no requirement on the pattern
type of the tasks. In addition, SRFDS not only preserves a closed-form solution
but also greatly improves the classification performance. Promising results on
various datasets serve as the evidence of better performance of SRFDS than
other SR-based methods. The Matlab code of SRFDS will be accessible at
http://www.yongxu.org/lunwen.html