Representing data by sparse combination of contextual data points for classification
This work addresses classification tasks by improving data representation through contextual learning, but it appears incremental as it builds on existing context-based methods with specific optimizations.
The paper tackles the problem of classifying data points by representing them as sparse linear reconstructions of their contextual data points, learning both the sparse context and a linear classifier in a unified supervised framework. Experiments on three benchmark datasets demonstrate its advantage over state-of-the-art context-based methods.
In this paper, we study the problem of using contextual da- ta points of a data point for its classification problem. We propose to represent a data point as the sparse linear reconstruction of its context, and learn the sparse context to gather with a linear classifier in a su- pervised way to increase its discriminative ability. We proposed a novel formulation for context learning, by modeling the learning of context reconstruction coefficients and classifier in a unified objective. In this objective, the reconstruction error is minimized and the coefficient spar- sity is encouraged. Moreover, the hinge loss of the classifier is minimized and the complexity of the classifier is reduced. This objective is opti- mized by 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.