Many applications in computer vision and pattern recognition involve drawing inferences on certain manifoldvalued parameters. In order to develop accurate inference algorithms on ...
Pavan K. Turaga, Ashok Veeraraghavan, Rama Chellap...
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope w...
In this paper, we present an approach we refer to as "least squares congealing" which provides a solution to the problem of aligning an ensemble of images in an unsuperv...
Mark Cox, Sridha Sridharan, Simon Lucey, Jeffrey F...
Over the years, many tensor based algorithms, e.g. two dimensional principle component analysis (2DPCA), two dimensional singular value decomposition (2DSVD), high order SVD, have...