J
Jing Gao
Researcher at Purdue University
Publications - 231
Citations - 13407
Jing Gao is an academic researcher from Purdue University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 52, co-authored 223 publications receiving 10438 citations. Previous affiliations of Jing Gao include State University of New York System & Association for Computing Machinery.
Papers
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Journal ArticleDOI
Outlier Detection for Temporal Data: A Survey
TL;DR: A comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used is provided.
Proceedings ArticleDOI
Multi-view clustering via joint nonnegative matrix factorization
TL;DR: This paper proposes a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views and designs a novel and effective normalization strategy inspired by the connection between NMF and PLSA.
Proceedings ArticleDOI
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
TL;DR: An end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events, is proposed.
Proceedings ArticleDOI
Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation
TL;DR: This paper proposes to resolve conflicts among multiple sources of heterogeneous data types by using an optimization framework where truths and source reliability are defined as two sets of unknown variables and the objective is to minimize the overall weighted deviation between the truths and the multi-source observations.
Journal ArticleDOI
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
TL;DR: This work proposes a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive.