Regression Reformulations of LLE and LTSA With Locally Linear Transformation

Abstract

Locally linear embedding (LLE) and local tangent space alignment (LTSA) are two fundamental algorithms in manifold learning. Both LLE and LTSA employ linear methods to achieve their goals but with different motivations and formulations. LLE is developed by locally linear reconstructions in both high- and low-dimensional spaces, while LTSA is developed with the combinations of tangent space projections and locally linear alignments. This paper gives the regression reformulations of the LLE and LTSA algorithms in terms of locally linear transformations. The reformulations can help us to bridge them together, with which both of them can be addressed into a unified framework. Under this framework, the connections and differences between LLE and LTSA are explained. Illuminated by the connections and differences, an improved LLE algorithm is presented in this paper. Our algorithm learns the manifold in way of LLE but can significantly improve the performance. Experiments are conducted to illustrate this fact.