Task-Driven Dictionary Learning

TL;DR

This paper introduces a supervised dictionary learning framework with an efficient algorithm, demonstrating its effectiveness across various large-scale classification, regression, and inverse problems involving sparse data representations.

Contribution

It proposes a general supervised dictionary learning formulation and an efficient optimization algorithm applicable to diverse tasks and data types.

Findings

Effective in large-scale classification and regression tasks

Suitable for supervised and semi-supervised learning

Demonstrated success in diverse applications like digit classification and image inverse problems

Abstract

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.