Group based non-sparse localized multiple kernel learning algorithm for image classification

• Published in: 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS) pp. 191 - 195
• Main Authors: Guangyuan Fu, Qingchao Wang, Hongqiao Wang, Dongying Bai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-08-2016
• Subjects: Bayes methods; Classification algorithms; Clustering algorithms; Kernel; Learning systems; local learning; multiple kernel learning; non-sparse; support vector machine; Support vector machines; Training
• DOI: 10.1109/CCIS.2016.7790251

Multiple kernel learning is a new research focus in the field of kernel machine learning in recent years. Localized multiple kernel learning is a promising strategy for combining multiple features or kernels in terms of their discriminative power for different local space. In this paper, we proposed a group based non-sparse localized multiple kernel learning algorithm for image classification. There are two steps in our algorithm. In the first step, the samples are divided into groups according to a clustering algorithm. In the second step, the SVM model and local kernel weights are optimized by turns. By the process of clustering, both inter-cluster correlation and intra-cluster diversity are taken into concern. Since the Ip norm constraint is employed on the kernel weights, a non-sparse result of kernels is obtained. The performance of classifier is improved by adjusting the sparsity of kernels. The experiment on the synthetic data set shows that our method obtains a better decision boundary; the experiments on the image sets verify the improvement of classification accuracies and training speed.