A prototype selection method based on kernel extreme learning machine in large-scale multi-label learning

With a largeamount of multimedia content in the web, storage and retrieval of them by classical learning methods dealt with some major challenges like memory restriction. These limitations in some of learning algorithms like SVM and ANN is so serious that these algorithms cannot be employed in large-scale learning context. Kernel Extreme Learning Machine (KELM) algorithm is one of the powerful methods in machine learning. Learning phase of this method is based on constructing kernel matrix of labeled instances and calculating inverse of it. So, employing this method in large scale learning context with a lot of labeled instances is not feasible. In this research to overcome limitation of employing the KELM in large-scale multi-label learning, a new approach is proposed. The proposed approach is based on prototype selection in neighborhood of each training instance. By using the proposed approach, the size of training set is reduced. So, classical learning methods can be applied on reduced training set. Since multimedia contents are basically multi-label, the proposed prototype selection approach is based on multi-label domains like automatic image annotation. Experimental results on NUS-WIDE large-scale multi-label image set and three other versions include Object, Scene and Lite indicated the effectiveness of the proposed approach in solving the limitation of employing KELM method in large-scale multi-label learning.