seqamlab

Shape analysis is an important process for many computer vision applications, including image classification, recognition, retrieval, registration, segmentation, etc. An ideal shape model should be both invariant to global transformations and robust to local distortions. In this work we developed … Read the rest

Nonlinear Dimensionality Reduction for Regression [1]

The task of dimensionality reduction for regression (DRR) is to find a low dimensional representation, z (q-dim), of the input covariates, x (p-dim), with q << p, for regressing the output, y (d-dim), given … Read the rest

Gaussian Process Manifold Kernel Dimensionality Reduction (GPMKDR) [1]

Constructing optimal regressors is an important task in computer vision. Problems such as tracking, 3D human and general object pose estimation, image and signal denoising, illumination direction estimation are but some of … Read the rest

Discriminative Graphical Models

In Collaboration with Dr. James M. Rehg and Yushi Jing, College of Computing, Georgia Institute of Technology.

Discriminative learning, or learning for classification, is a common learning task that has been addressed in a number of different … Read the rest

 

The brain mechanisms underlying the ability of humans to process faces have been studied extensively in the last two decades. Brain imaging techniques, particularly fMRI (functional Magnetic Resonance Imaging) that possesses high spatial resolution but limited temporal resolution, are advancing … Read the rest

Protein Classification and Functional Prediction 
Problem 

Proteins are linear seqeuences of amino acids that perform essential functions in organisms. To determine the function of a protein, expensive experimental procedures are required. With better sequencing techniques, it becomes evident that computational … Read the rest

Kernel Methods for Sequence Classification

DNA Barcoding

Genomic approaches to classification of organisms exploit diversity among DNA sequences to identify organisms. The task of identification is to assign any unidentified organism to the taxonomic group at the target level: class, … Read the rest

Fast Semi-Supervised Homology Prediction using Sparse Spatial Sample Kernel

Motivation:

Establishing structural and/or functional relationship between sequences, for instance, to infer the structural class of an unannotated protein, is a key task in a biological sequence analysis. Recent methods such … Read the rest