2 citations · 3 across the 4 of their papers we have counts for
4 papers
Probabilistic Dimensionality Reduction via Structure Learning
Li Wang
We propose a novel probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework,…
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
Qi Mao, Li Wang, Ivor W. Tsang +1
Many scientific datasets are of high dimension, and the analysis usually requires visual manipulation by retaining the most important structures of data. Principal curve is a widel…
Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals
Mingkui Tan, Ivor W. Tsang, Li Wang
Matching Pursuit LASSIn Part I \cite{TanPMLPart1}, a Matching Pursuit LASSO ({MPL}) algorithm has been presented for solving large-scale sparse recovery (SR) problems. In this pape…
Towards Ultrahigh Dimensional Feature Selection for Big Data
Mingkui Tan, Ivor W. Tsang, Li Wang
In this paper, we present a new adaptive feature scaling scheme for ultrahigh-dimensional feature selection on Big Data. To solve this problem effectively, we first reformulate it…