91 citations · 178 across the 17 of their papers we have counts for
5 papers · 1 filter
Manifold Learning via Manifold Deflation
Daniel Ting, Michael I. Jordan
Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on s…
On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding
Daniel Ting, Michael I. Jordan
We develop theory for nonlinear dimensionality reduction (NLDR). A number of NLDR methods have been developed, but there is limited understanding of how these methods work and the…
Optimal Sub-sampling with Influence Functions
Daniel Ting, Eric Brochu
Sub-sampling is a common and often effective method to deal with the computational challenges of large datasets. However, for most statistical models, there is no well-motivated ap…
Adaptive Threshold Sampling
Daniel Ting
Sampling is a fundamental problem in computer science and statistics. However, for a given task and stream, it is often not possible to choose good sampling probabilities in advanc…
An Analysis of the Convergence of Graph Laplacians
Daniel Ting, Ling Huang, Michael Jordan
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumptio…