Abstract It is known that purely geometric distance metrics cannot reflect the human perception of facial expressions. A novel perceptually based metric designed for 3D blendshape models proposed in this paper. To develop metric, comparative evaluations expressions were collected from a crowdsourcing experiment. Then, weights on descriptive features models, optimized to match results with crowd...

Many supervised and unsupervised learning algorithms depend on the choice of an appropriate distance metric. While metric learning for supervised learning tasks has a long history, extending it to learning tasks with weaker supervisory information has only been studied very recently. In particular, several methods have been proposed for semi-supervised metric learning based on pairwise (dis)sim...

Clustering has been used in various fields, such as image processing, data mining, pattern recognition, and statistical analysis. Generally, clustering algorithms consider all variables equally relevant or not correlated. Nevertheless, the of samples multidimensional space can be geometrically complicated, e.g., clusters may exist different subsets features. In this regard, new soft subspace ha...

(x − x)A(x − x) Mahalanobis distance: Previous work on learning metrics has focused on learning a single distance metric for all instances. One of our primary contributions is to learn a distance function for every training image. Most visual categorization approaches make use of machine learning after computing distances between images (e.g. SVM with pyramid kernel). We want to learn how to co...

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive d...

We describe a latent variable model for supervised dimensionality reduction and distance metric learning. The model discovers linear projections of high dimensional data that shrink the distance between similarly labeled inputs and expand the distance between differently labeled ones. The model’s continuous latent variables locate pairs of examples in a latent space of lower dimensionality. The...