Clustering, coding, and the concept of similarity

Cites Work

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Intrinsic statistics on Riemannian manifolds: Basic tools for geometric measurements
Stochastic calculus in manifolds. With an appendix by P.A. Meyer
Stochastic differentials
Diffusion maps
Hypoelliptic second order differential equations
Learning Deep Architectures for AI
The estimation of the gradient of a density function, with applications in pattern recognition
Markov Processes from K. Ito's Perspective (AM-155)
Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Gaussian measures in traditional and not so traditional settings
A Connection Between Score Matching and Denoising Autoencoders
Spectral Connectivity Analysis
Space-Time Approach to Non-Relativistic Quantum Mechanics
Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
Learning Theory
A Fast Learning Algorithm for Deep Belief Nets
A New Representation for Stochastic Integrals and Equations
On the growth of stochastic integrals
On Distributions of Certain Wiener Functionals
Probability Theory
Stochastic differential equations. An introduction with applications.
On lines and planes of closest fit to systems of points in space.

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