Supervised learning of sparse context reconstruction coefficients for data representation and classification
This addresses classification tasks where context is important, offering an incremental improvement over existing context-based methods.
The paper tackles the problem of using data point context for classification by representing each point as a sparse linear combination of its context, learning this in a supervised way to enhance discriminative ability. Experiments on three benchmark datasets show it outperforms state-of-the-art context-based methods.
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.