Locally linear embedding for classification
Locally linear embedding (LLE) is a recently proposed unsupervised
procedure for mapping high-dimensional data nonlinearly to a lower-
dimensional space. In this paper, a supervised variation on LLE is
proposed. This mapping, when combined with simple classifiers such as
the nearest mean classifier, is shown to yield remarkably good
classification results in experiments. Furthermore, a number of
algorithmic improvements are proposed which should ease application of
both traditional and supervised LLE by eliminating the need for setting
some of the parameters.