This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that videos can be regarded as data points in embedding manifold subspace, and their matching problem quantified through specific Grassmannian kernel function while integrating correlation exploration similarity measurement into joint framework. By maximizing the intra-class compactness ...

Abstract This paper presents a collection of 350 000 German lemmatised words, rated on four psycholinguistic affective attributes. All ratings were obtained via a supervised learning algorithm that can automatically calculate a numerical rating of a word. We applied this algorithm to abstractness, arousal, imageability and valence. Comparison with human ratings reveals high correlation across a...

With the growing interest in opinion mining from web data, more works are focused on mining in English and Chinese reviews. Probing into the problem of product opinion mining, this paper describes the details of our language resources, and imports them into the task of extracting product feature and sentiment task. Different from the traditional unsupervised methods, a supervised method is util...

In this thesis, a study on gene expression data analysis is done using some supervised, unsupervised and semi-supervised approaches. The task of class prediction for six gene expression datasets (namely, Brain Tumor, Colon Cancer, Leukemia, Lymphoma and SRBCT) has been carried out. Here, a one-dimensional self-organizing feature maps (SOFM) in a semi-supervised learning framework is developed f...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a n...