Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis (PCA) has b...
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinto...
Face recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snapshot about the ...
The dimensionality of face space is measured objectively in a psychophysical study. Within this framework we obtain a measurement of the dimension for the human visual system. Usi...
: While feature selection is very difficult for high dimensional, unstructured data such as face image, it may be much easier to do if the data can be faithfully transformed into l...