Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition

We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. In this paper, we introduce the semi-supervised virtual evidence boosting (sVEB) algorithm for training CRFs – a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. The objective function of sVEB combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. It reduces the overall system cost as well as the human labeling cost required during training, which are both important con...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Maryam Mahdaviani, Tanzeem Choudhury
• Keywords: Efficient Semi-supervised Training, Feature Selection, Information Technology, NIPS 2007, Semi-supervised Virtual Evidence