We propose a new dimensionality reduction method, the elastic embedding (EE), that optimises an intuitive, nonlinear objective function of the low-dimensional coordinates of the d...
A new efficient dynamic programming (DP) algorithm for 2D elastic matching is proposed. The present DP algorithm requires by far less complexity than previous DPbased elastic mat...
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
In the last decades, a large family of algorithms supervised or unsupervised; stemming from statistic or geometry theory have been proposed to provide different solutions to the p...