16 citations · 66 across the 38 of their papers we have counts for
4 papers · 1 filter
Consistent Representation Learning for High Dimensional Data Analysis
Stan Z. Li, Lirong Wu, Zelin Zang
High dimensional data analysis for exploration and discovery includes three fundamental tasks: dimensionality reduction, clustering, and visualization. When the three associated ta…
Deep Manifold Transformation for Nonlinear Dimensionality Reduction
Stan Z. Li, Zelin Zang, Lirong Wu
Manifold learning-based encoders have been playing important roles in nonlinear dimensionality reduction (NLDR) for data exploration. However, existing methods can often fail to pr…
Invertible Manifold Learning for Dimension Reduction
Siyuan Li, Haitao Lin, Zelin Zang +3
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information. In the context of manifold learning,…
Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation
Lirong Wu, Zicheng Liu, Zelin Zang +3
Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss…