Semi-supervised clustering is a constrained technique that organizes collection of unlabeled data into homogeneous subgroups with the help domain knowledge expressed as constraints. These methods are, most time, variants popular k-means algorithm. As such, they are based on criterion to minimize. Amongst existing semi-supervised clusterings, Evidential Clustering (SECM) deals problem uncertain/...

Following a discussion on the general form of regularization for semi-supervised learning, we propose a semi-supervised regression algorithm. It is based on the assumption that we have certain order preferences on unlabeled data (e.g., point x1 has a larger target value than x2). Semi-supervised learning consists of enforcing the order preferences as regularization in a risk minimization framew...

Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain specified semantics in an untrimmed video. TLG is inherently challenging task, as it requires comprehensive understanding both sentence and video contents. Previous works either tackle this task fully-supervised setting large amount annotations or weakly-supervised usual...