17 citations · 38 across the 9 of their papers we have counts for
9 papers
Clustering and Community Detection with Imbalanced Clusters
Cem Aksoylar, Jing Qian, Venkatesh Saligrama
Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalan…
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
Jonathan Root, Venkatesh Saligrama, Jing Qian
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on -point nominal training data. We…
Learning Efficient Anomaly Detectors from -NN Graphs
Jing Qian, Jonathan Root, Venkatesh Saligrama
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average -NN distance, and rank them accordingly. We then train…
Efficient Minimax Signal Detection on Graphs
Jing Qian, Venkatesh Saligrama
Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph. In these application…
A Rank-SVM Approach to Anomaly Detection
Jing Qian, Jonathan Root, Venkatesh Saligrama +1
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from neares…
Spectral Clustering with Imbalanced Data
Jing Qian, Venkatesh Saligrama
Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present. We show that Ratio-Cut (RCut) or normalized…
Spectral Clustering with Unbalanced Data
Jing Qian, Venkatesh Saligrama
Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and…
Graph-based Learning with Unbalanced Clusters
Jing Qian, Venkatesh Saligrama, Manqi Zhao
Graph construction is a crucial step in spectral clustering (SC) and graph-based semi-supervised learning (SSL). Spectral methods applied on standard graphs such as full-RBF, -g…
Graph Construction for Learning with Unbalanced Data
Jing Qian, Venkatesh Saligrama, Manqi Zhao
Unbalanced data arises in many learning tasks such as clustering of multi-class data, hierarchical divisive clustering and semisupervised learning. Graph-based approaches are popul…