A new locally linear embedding scheme in light of Hessian eigenmap
This work addresses incremental improvements in dimensionality reduction methods for machine learning practitioners.
The paper tackles the complexity and robustness issues of Hessian locally linear embedding (HLLE) by reinterpreting it as a variant of locally linear embedding (LLE), leading to a simplified method called tangential LLE (TLLE) that is simpler and more robust.
We provide a new interpretation of Hessian locally linear embedding (HLLE), revealing that it is essentially a variant way to implement the same idea of locally linear embedding (LLE). Based on the new interpretation, a substantial simplification can be made, in which the idea of "Hessian" is replaced by rather arbitrary weights. Moreover, we show by numerical examples that HLLE may produce projection-like results when the dimension of the target space is larger than that of the data manifold, and hence one further modification concerning the manifold dimension is suggested. Combining all the observations, we finally achieve a new LLE-type method, which is called tangential LLE (TLLE). It is simpler and more robust than HLLE.