Iterated Diffusion Maps for Feature Identification

TL;DR

This paper extends diffusion map theory to represent degenerate mappings and introduces an iterated diffusion map approach that emphasizes features and reduces irrelevant dimensions in data manifolds.

Contribution

It generalizes local kernel theory for degenerate mappings and develops an iterative diffusion map method for feature identification and dimension reduction.

Findings

IDM converges to the quotient manifold representing the feature.

The method estimates the tangent space and intrinsic dimension accurately.

Empirical results show improved feature representation and dimension reduction.

Abstract

Recently, the theory of diffusion maps was extended to a large class of local kernels with exponential decay which were shown to represent various Riemannian geometries on a data set sampled from a manifold embedded in Euclidean space. Moreover, local kernels were used to represent a diffeomorphism, H, between a data set and a feature of interest using an anisotropic kernel function, defined by a covariance matrix based on the local derivatives, DH. In this paper, we generalize the theory of local kernels to represent degenerate mappings where the intrinsic dimension of the data set is higher than the intrinsic dimension of the feature space. First, we present a rigorous method with asymptotic error bounds for estimating DH from the training data set and feature values. We then derive scaling laws for the singular values of the local linear structure of the data, which allows the identification the tangent space and improved estimation of the intrinsic dimension of the manifold and the bandwidth parameter of the diffusion maps algorithm. Using these numerical tools, our approach to feature identification is to iterate the diffusion map with appropriately chosen local kernels that emphasize the features of interest. We interpret the iterated diffusion map (IDM) as a discrete approximation to an intrinsic geometric flow which smoothly changes the geometry of the data space to emphasize the feature of interest. When the data lies on a product manifold of the feature manifold with an irrelevant manifold, we show that the IDM converges to the quotient manifold which is isometric to the feature manifold, thereby eliminating the irrelevant dimensions. We will also demonstrate empirically that if we apply the IDM to features that are not a quotient of the data space, the algorithm identifies an intrinsically lower-dimensional set embedding of the data which better represents the features.