28 citations · 31 across the 6 of their papers we have counts for
5 papers · 1 filter
K-means Derived Unsupervised Feature Selection using Improved ADMM
Ziheng Sun, Chris Ding, Jicong Fan
Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of…
Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection
Wenwen Min, Taosheng Xu, Chris Ding
Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a…
A Closed Form Solution to Multi-View Low-Rank Regression
Shuai Zheng, Xiao Cai, Chris Ding +2
Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity…
Kernel Alignment Inspired Linear Discriminant Analysis
Shuai Zheng, Chris Ding
Kernel alignment measures the degree of similarity between two kernels. In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulati…
An Iterative Locally Linear Embedding Algorithm
Deguang Kong, Chris H. Q. Ding, Heng Huang +1
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedd…