70 citations · 179 across the 19 of their papers we have counts for
7 papers · 1 filter
: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering
Ke Alexander Wang, Xinran Bian, Pan Liu +1
Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can…
Similarity Kernel and Clustering via Random Projection Forests
Donghui Yan, Songxiang Gu, Ying Xu +1
Similarity plays a fundamental role in many areas, including data mining, machine learning, statistics and various applied domains. Inspired by the success of ensemble methods and…
Learning over inherently distributed data
Donghui Yan, Ying Xu
The recent decades have seen a surge of interests in distributed computing. Existing work focus primarily on either distributed computing platforms, data query tools, or, algorithm…
A First Course in Data Science
Donghui Yan, Gary E. Davis
Data science is a discipline that provides principles, methodology and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many a…
Fast communication-efficient spectral clustering over distributed data
Donghui Yan, Yingjie Wang, Jin Wang +2
The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typicall…
A Structured Approach to the Analysis of Remote Sensing Images
Donghui Yan, Congcong Li, Na Cong +2
The number of studies for the analysis of remote sensing images has been growing exponentially in the last decades. Many studies, however, only report results---in the form of cert…
Cost-sensitive Selection of Variables by Ensemble of Model Sequences
Donghui Yan, Zhiwei Qin, Songxiang Gu +2
Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often…