On the Equivalence of HLLE and LTSA.

Abstract

Among the representative algorithms of manifold learning, Hessian locally linear embedding (HLLE) and local tangent space alignment (LTSA) algorithms haven been regarded as two different algorithms. However, in practice, the effects of these two algorithms are very similar and LTSA performs better than HLLE in some applications. This paper tries to account for this phenomenon from a mathematical point of view. There are only two differences between HLLE and LTSA. First, LTSA includes a data point into its neighborhood, while HLLE does not. Second, HLLE and LTSA use different methods to align the local coordinates of manifold. In this paper, we show that, the first difference between HLLE and LTSA is not essential. However, from the viewpoint of data utilization, LTSA does better than HLLE in the neighborhood construction. This may account for why LTSA can perform better than HLLE in some applications. As for the second difference between HLLE and LTSA, we first prove that, the alignment equations used by HLLE and LTSA are exactly the same. Second, we prove that, although HLLE and LTSA uses different methods to solve the alignment equation, their solutions are exactly the same, provided that HLLE adopts the same method as LTSA to construct the neighborhoods. Based on these arguments, we claim that HLLE and LTSA are equivalent to each other. This conclusion can also be verified experimentally by using manifold learning MATLAB demo (MANI), a widely-used experimental platform of manifold learning. When testing HLLE on MANI, if HLLE adopts the same method as LTSA to construct the neighborhoods, the experimental results presented by MANI will be the same as those of LTSA.