We consider the semi-supervised clustering problem where we know (with varying degree of certainty) that some sample pairs are (or are not) in the same class. Unlike previous efforts in adapting clustering algorithms to incorporate those pairwise relations, our work is based on a discriminative model. We generalize the standard Gaussian process classifier (GPC) to express our classification pre...

This paper presents a generative model based on the language modeling approach for sentiment analysis. By characterizing the semantic orientation of documents as “favorable” (positive) or “unfavorable” (negative), this method captures the subtle information needed in text retrieval. In order to conduct this research, a language model based method is proposed to keep the dependent link between a...

This paper addresses the problem of joint recognition and localization of actions in videos. We develop a novel Transfer Latent Support Vector Machine (TLSVM) by using Web images and weakly annotated training videos. In order to alleviate the laborious and timeconsuming manual annotations of action locations, the model takes training videos which are only annotated with action labels as input. ...

This paper proposes a novel technique to exploit discriminative models with subclasses for speech recognition. Speech recognition using discriminative models has attracted much attention in the past decade. However, most discriminative models are still based on tree clustering results of HMM states. On the contrary, our proposed method, referred to as subclass AdaBoost, jointly selects optimal ...

Human action recognition from video sequences is a challenging problem due to the large changes of human appearance in the cases of partial occlusions, non-rigid deformations, and high irregularities. It is difficult to collect a large set of training samples to learn the discriminative model with covering all possible variations of an action. In this paper, we propose an online recognition met...

Since recognition errors are unavoidable in speech recognition, confidence scoring, which accurately estimates the reliability of recognition results, is a critical function for speech recognition engines. In addition to achieving accurate confidence estimation, if we are to develop speech recognition systems that will be widely used by the public, speech recognition engines must be able to rep...

The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition. Devising a representation suitable for characterising actions on the basis of noisy skeleton sequences remains a challenge, however. We here provide two insights into this challenge. First, we show that the discriminative information of a skeleton sequence usually resides in a short ...

We present an online discriminative training approach to grapheme-to-phoneme (g2p) conversion. We employ a manyto-many alignment between graphemes and phonemes, which overcomes the limitations of widely used one-to-one alignments. The discriminative structure-prediction model incorporates input segmentation, phoneme prediction, and sequence modeling in a unified dynamic programming framework. T...

In the field of face recognition it is generally believed that ”state of the art” recognition rates can only be achieved when discriminative (e.g., linear or generalized discriminant analysis) rather than expressive (e.g., principal or kernel principal component analysis) methods are used for facial feature extraction. However, while being superior in terms of the recognition rates, the discrim...