Abstract
In this paper, we introduce a new classification approach that learns class dependent Gaussian kernels and the belongingness likelihood of the data points with respect to each class. The proposed Support Kernel Classification (SKC) is designed to characterize and discriminate between the data instances from the different classes. It relies on the maximization of the intra-class distances and the minimization of the intra-class distances to learn the optimal Gaussian parameters. In fact, a novel objective function is proposed to model each class using one Gaussian function. The experiments conducted using synthetic datasets demonstrated the effectiveness of the proposed algorithm. Moreover, the results obtained using real datasets proved that the proposed classifier outperforms the relevant state of the art approaches.
Highlights
Classification finds applications in many real-world problems related to different fields
The Gaussian parameters are selected based on the value of a criterion function that is computed on the training data [11
Based on the comparison of the classification results obtained by Support Kernel Classification (SKC) and Gaussian Mixture Model (GMM) using the different synthetic datasets, one can claim that the learning of the optimal Gaussian parameters using the intra-class and the inter-class characteristics of each dataset, makes SKC outperform GMM
Summary
Classification finds applications in many real-world problems related to different fields. Kernel-based approaches [8] have been proposed as an alternative solution They map the data into a new feature space in such a way that categorizing classes with complex boundaries can be reduced to a simple categorization problem in the new feature space. We propose a novel classification algorithm named the Support Kernel Classifier (SKC). It categorizes the data by learning a Gaussian kernel for each category. The proposed classifier learns the probability of each data point to belong to each class It does not use crisp assignment where an instance belongs or not to a class, but rather learns its likelihood to belong to it.
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