A Sampling Theory Perspective of Graph-Based Semi-Supervised Learning

A Sampling Theory Perspective of Graph-based Semi-supervised Learning

Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the dataset. It is often desirable for the constructed graph to satisfy two properties: first, data points that are similar in the feature space should be strongly connected on the graph, and second, the class label informati...

Graph-Based Semi-Supervised Learning

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in ...

Parallel Graph-Based Semi-Supervised Learning

Semi-supervised learning (SSL) is the process of training decision functions using small amounts of labeled and relatively large amounts of unlabeled data. In many applications, annotating training data is time-consuming and error prone. Speech recognition is the typical example, which requires large amounts of meticulously annotated speech data (Evermann et al., 2005) to produce an accurate sy...

Semi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk

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...

Hyper-parameter Learning for Graph Based Semi-supervised Learning Algorithms