70 citations · 179 across the 19 of their papers we have counts for
4 papers · 1 filter
A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis
Rui Ming, Haiping Xu, Shannon E. Gibbs +2
Deep learning approaches require collection of data on many different input features or variables for accurate model training and prediction. Since data collection on input feature…
: 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…
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…