# Semi-supervised dual graph regularized dictionary learning

## TL;DR

This paper introduces a semi-supervised dictionary learning method that leverages both labeled and unlabeled data, preserving data manifold structure in sparse codes to improve classification performance.

## Contribution

It presents a novel semi-supervised approach that integrates manifold regularization with dictionary learning and joint classifier training, enhancing predictive accuracy.

## Key findings

- Significant improvement over existing methods.
- Manifold preservation enhances classification accuracy.
- Nonlinear classifiers further boost performance.

## Abstract

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.