Research Of The Hypergraph And Its Application In Image Classification

In machine learning problem settings, we generally assume pairwise relationships among the objects of our interest. An object set endowed with pairwise relationships can be naturally illustrated as a graph, in which the vertices represent the objects, and any two vertices that have some kind of relationship are joined together by an edge. In real-world, we are interested in the relationship between the objects more than the relationship between two objects, but even more complex multivariate relationships. If we simply put into a compressed multivariate relationships between pairwise sequence relationship, which will inevitably lose a lot of useful information and that will cause a certain degree of influence on the accuracy of the machine learning algorithm. A hypergraph edge contain multiple nodes, thus it contains more information than a normal graph. We use hypergraph to represent the complex relationships between objects we are interested in not only to ensure an accurate description of the relationship between the objects but also to ensure the accuracy of the machine learning algorithm.This paper studies the basic properties of hypergraph and the segmentation, random walk, spectrum segmentation, iterative methods and so on. We propose a hypergraph learning algorithm based feature selection method for indoor scene classification. It performs feature selection by hypergraph regularization, which not only considers the interaction among features but also the interaction between the feature selection heuristics and the corresponding classifier. For the convenience of the prediction of the new images, a liner regression model is integrated in the framework, making the new images classification directly and in real time. The experimental results show that our approach has satisfactory performance compared with previously proposed methods.