Distributional models are derived from cooccurrences in a corpus, where only a small proportion of all possible plausible cooccurrences will be observed. This results in a very sparse vector space, requiring a mechanism for inferring missing knowledge. Most methods face this challenge in ways that render the resulting word representations uninterpretable, with the consequence that semantic comp...

With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for each microbiome sample. One goal of microbiome study is to associate the microbiome composition with environmental covariates. We propose to model the taxa counts using a Dirichlet-multinomial (DM) r...

Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image...

Shape prior modeling is a challenging and crucial component in various image segmentation applications. Most existing methods aim at dealing with single object’s shape variation, which are not directly applicable for multishape prior modeling. In this paper, we present an extension of recently proposed Spare Shape Composition model (SSC) for multi-shape prior modeling. In this extension, multip...

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling m...

Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent ye...