Recently, it was shown that there is a phase transition in the community detection problem. This transition was first computed using the cavity method, and has been proved rigorously in the case of q = 2 groups. However, analytic calculations using the cavity method are challenging since they require us to understand probability distributions of messages. We study analogous transitions in so-ca...

We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning. Our DAN approach builds upon generative adversarial networks (GANs) and conditional GANs but includes the key differentiator of using two discriminators instead of a generator and a discriminator. DAN can be seen as a framework to learn loss functions for predictors that also implements...

Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse densit...

A major challenge in pattern recognition is labeling of large numbers of samples. This problem has been solved by extending supervised learning to semi-supervised learning. Thus semi-supervised learning has become one of the most important methods on the research of facial expression recognition. Frontal and un-occluded face images have been well recognized using traditional facial expression r...

Semi-supervised clustering is an important method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering based on Mahalanobis distance and Gaussian Kernel for SCAPC algorithm. Here, we give a new semi-supervised fuzzy clustering objective function. By solving the optimization problem with above objec...

We present a novel self-supervised learning approach for conditional generative adversarial networks (GANs) under semi-supervised setting. Unlike prior approaches which often involve geometric augmentations on the image space such as predicting rotation angles, our pretext task leverages label space. perform augmentation by randomly sampling sensible labels from of few labelled examples availab...

Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an open issue. In this paper a novel SVMKNN classification methodology based on Semi-supervised learning is proposed, we consider the problem of using a large number of unlabeled data to boost performance of the classifier when only a small set of labeled examples is ava...

Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric and similarity-centric approaches, and proposes a filtered ranking method that combines the advantages of the two. We use a range of extraction tasks to compare the ...

We consider the task of learning a classifier from the feature space X to the set of classes Y = {0, 1}, when the features can be partitioned into class-conditionally independent feature sets X1 and X2. We show that the class-conditional independence can be used to represent the original learning task in terms of 1) learning a classifier from X2 to X1 (in the sense of estimating the probability...

Recognizing speech act types in Twitter is of much theoretical interest and practical use. Our previous research did not adequately address the deficiency of training data for this multi-class learning task. In this work, we set out by assuming only a small seed training set and experiment with two semi-supervised learning schemes, transductive SVM and graph-based label propagation, which can l...