Supervised learning of sparse context reconstruction coefficients for data representation and classification

TL;DR

This paper introduces a supervised method for learning sparse context-based representations of data points, improving classification accuracy by focusing on critical contextual data through an integrated optimization approach.

Contribution

It proposes a novel unified formulation for supervised sparse context learning that jointly optimizes context parameters and classifiers, enhancing data representation and classification.

Findings

Outperforms state-of-the-art context-based methods on benchmark datasets

Demonstrates the effectiveness of sparse context modeling in classification tasks

Provides an iterative algorithm for joint optimization of context and classifier

Abstract

Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context, and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.