Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors...
A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2dimensional Riemannian manifolds that are statistically defined by maps that...
Sang-Mook Lee, A. Lynn Abbott, Neil A. Clark, Phil...
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside ...
Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data poin...
The construction of low-dimensional models explaining highdimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern ...