Semi-supervised dual graph regularized dictionary learning
This work addresses semi-supervised learning for applications needing efficient data utilization, but it appears incremental as it builds on existing methods like LLE.
The paper tackled the problem of semi-supervised dictionary learning by incorporating both labeled and unlabeled data to preserve manifold structure in sparse code space, resulting in significant improvements over other methods, with further gains possible using an SVM classifier.
In this paper, we propose a semi-supervised dictionary learning method that uses both the information in labelled and unlabelled data and jointly trains a linear classifier embedded on the sparse codes. The manifold structure of the data in the sparse code space is preserved using the same approach as the Locally Linear Embedding method (LLE). This enables one to enforce the predictive power of the unlabelled data sparse codes. We show that our approach provides significant improvements over other methods. The results can be further improved by training a simple nonlinear classifier as SVM on the sparse codes.