Results 231 to 240 of about 53,359 (288)

Protocol for integrating and interpreting multi-omics data combining unsupervised and supervised data integrating approaches. [PDF]

STAR Protoc
Anagho-Mattanovich M, Anagho-Mattanovich HA, Gao Q, Moritz T.   +3 more
europepmc   +1 more source

From Analysis to Assessment: Machine Learning for Non-Target Screening of Pollutants Using Chromatography Coupled with (Ion Mobility) Mass Spectrometry. [PDF]

Toxics
Lin D, Wang Z, Liao J, Li N, Li X.
europepmc   +1 more source

Age-Stratified Modeling of Clinical Heterogeneity in Polycystic Ovary Morphology Using Ultrasound-Based Machine Learning. [PDF]

Int J Womens Health
Kuang C, Wei Q, Liu Z, Zhang Y, Zhong Z, Hu N, Xiao C, Bao L, Xiao Y, Du M, Chen Z.   +10 more
europepmc   +1 more source

Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions. [PDF]

Bioengineering (Basel)
Liu B, Li G, Zou Y, Zeng S, Lv J, Wu R, Zhang H.   +6 more
europepmc   +1 more source

Embedded Unsupervised Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Sparse learning has been proven to be a powerful techniquein supervised feature selection, which allows toembed feature selection into the classification (or regression)problem. In recent years, increasing attentionhas been on applying spare learning in unsupervisedfeature selection.
Suhang Wang, Jiliang Tang, Huan Liu 0001
openaire   +2 more sources

Unsupervised Personalized Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Feature selection is effective in preparing high-dimensional data for a variety of learning tasks such as classification, clustering and anomaly detection. A vast majority of existing feature selection methods assume that all instances share some common patterns manifested in a subset of shared features.
Jundong Li, Liang Wu 0006, Harsh Dani, Huan Liu 0001   +3 more
openaire   +2 more sources

Unsupervised feature selection for attributed graphs

Expert Systems with Applications, 2021
Abstract Many real-world applications generate attributed graphs that contain both link structures and content information associated with nodes. Content information in real networks always contains high dimensional feature space. In recent years, unsupervised feature selection has been widely used in handling high dimensional data without label ...
Ruizhi Zhou, Lingfeng Niu, Hong Yang 0003   +2 more
openaire   +2 more sources