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Jiajia Jing

dblp:173/3797 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0002-2824-6905ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Security and privacy · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Representation and self-supervised learning · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
discriminant analysis		 0.212016
Comments on "Joint Global and Local Structure Discriminant Analysis" · IEEE Trans. Inf. Forensics Secur. 2016
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction › discriminant analysis
linear discriminant analysis		 0.212016
Comments on "Joint Global and Local Structure Discriminant Analysis" · IEEE Trans. Inf. Forensics Secur. 2016
Data mining
representation learning		 0.112016
Comments on "Joint Global and Local Structure Discriminant Analysis" · IEEE Trans. Inf. Forensics Secur. 2016

Methods — techniques the papers use, named apart from their topics

linear discriminant analysis · 0.5
YearPublicationVenuePosition
2016 Recursive Dimension Reduction for semisupervised learning
Qiaolin Ye, Tongming Yin, Shangbing Gao, Jiajia Jing, Cui-Ping Sun
 Neurocomputing4
2016 Comments on "Joint Global and Local Structure Discriminant Analysis"
abstract
Joint global and local structure discriminant analysis (JGLDA) is a recently-developed linear discriminant analysis approach, which considers the diversity of data across classes. In this communication, however, we will show that the discussion on the characterization of within-class locality and within-class diversity of previous efforts on local linear discriminant analysis (LLDA) and JGLDA is flawed.
Qiaolin Ye, Jiajia Jing
 IEEE Trans. Inf. Forensics Secur.2