A Semi-Supervised Approach to Perceived Age Prediction from Face Images

A Semi-Supervised Approach to Perceived Age Prediction from Face Images

We address the problem of perceived age estimation from face images, and propose a new semi-supervised approach involving two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster-representative samples for c...

Semi-supervised Estimation of Perceived Age from Face Images

We address the problem of perceived age estimation from face images and propose a new semi-supervised age prediction method that involves two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster representati...

Perceived Age Estimation from Face Images

Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

We propose a simple discrete time semi–supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross–sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from past dynamics and the unsupervised loss of predicting the neighborhood context in...

A Bayesian Approach to Semi-Supervised Learning

Recent research in automated learning has focused on algorithms that learn from a combination of tagged and untagged data. Such algorithms can be referred to as semi-supervised in contrast to unsupervised, which refers to algorithms requiring no tagged data whatsoever. This paper presents a Bayesian approach to semi-supervised learning. In this approach, the parameters of a probability model ar...