Semi-supervised dictionary learning with graph regularization and active points

TL;DR

This paper introduces a semi-supervised dictionary learning method that leverages graph regularization and active points to improve image classification accuracy with limited labeled data.

Contribution

It proposes a novel semi-supervised dictionary learning approach combining manifold preservation via Locally Linear Embedding and semi-supervised classification in sparse code space.

Findings

Improves classification accuracy over existing methods

Effectively utilizes unlabelled data for regularization

Preserves data manifold structure in sparse coding

Abstract

Supervised Dictionary Learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semi-supervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semi-supervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using Locally Linear Embedding, which can be considered a regularization of sparse code; on the other hand, we train a semi-supervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semi-supervised dictionary learning methods.