2015-05-14

One accepted paper by Neurocomputing is online now.

A general framework for co-training and its applications

Abstract

Co-training is one of the major semi-supervised learning paradigms in which two classifiers are alternately trained on two distinct views and they teach each other by adding the predictions of unlabeled data to the training set of the other view. Co-training can achieve promising performance, especially when there is only a small number of labeled data. Hence, co-training has received considerable attention, and many variant co-training algorithms have been developed. It is essential and informative to provide a systematic framework for a better understanding of the common properties and differences in these algorithms. In this paper, we propose a general framework for co-training according to the diverse learners constructed in co-training. Specifically, we provide three types of co-training implementations, including co-training on multiple views, co-training on multiple classifiers, and co-training on multiple manifolds. Finally, comprehensive experiments of different methods are conducted on the UCF-iPhone dataset for human action recognition and the USAA dataset for social activity recognition. The experimental results demonstrate the effectiveness of the proposed solutions.

Keywords

2015-05-09

One accepted paper is online now.

Manifold regularized kernel logistic regression for web image annotation

Abstract
With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.

Keywords
Manifold regularization; Kernel logistic regression; Laplacian Eigenmaps; Semi-supervised learning; Image annotation