Semi-Supervised Learning of Mixture Models

Generalized mixture models, semi-supervised learning, and unknown class inference

In this paper, we discuss generalized mixture models and related semi-supervised learning methods, and show how they can be used to provide explicit methods for unknown class inference. After a brief description of standard mixture modeling and current model-based semi-supervised learning methods, we provide the generalization and discuss its computational implementation using three-stage expec...

Boosting Mixture Models for Semi-supervised Learning

Multi-Instance Mixture Models and Semi-Supervised Learning

Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vector. Under standard assumptions, MI learning can be understood as a type of semisupervised learning (SSL). The difference between MI learning and SSL is that positive bag labels provide weak label information for the ins...

A Semi-Supervised Learning Algorithm Based on Modified Self-training SVM

In this paper, we first introduce some facts about semi-supervised learning and its often used methods such as generative mixture models, self-training, co-training and Transductive SVM and so on. Then we present a self-training semi-supervised SVM algorithm based on which we give out a modified algorithm. In order to demonstrate its validity and effectiveness, we carry out some experiments whi...

Semi-Supervised Learning with the Deep Rendering Mixture Model

Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a proba...