294 citations · 737 across the 61 of their papers we have counts for
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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…
Markov-Lipschitz Deep Learning
Stan Z. Li, Zelin Zang, Lirong Wu
We propose a novel framework, called Markov-Lipschitz deep learning (MLDL), to tackle geometric deterioration caused by collapse, twisting, or crossing in vector-based neural netwo…
A Foreground-background Parallel Compression with Residual Encoding for Surveillance Video
Lirong Wu, Kejie Huang, Haibin Shen +1
The data storage has been one of the bottlenecks in surveillance systems. The conventional video compression algorithms such as H.264 and H.265 do not fully utilize the low informa…