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Journal ArticleDOI
Principal Component Analysis Applied to Surface Electromyography: A Comprehensive Review
TL;DR: The role of PCA in conjunction with the quantitative sEMG analyses is disseminated to disseminate the technical challenges associated with the PCA-based s EMG processing, and the envisaged research trend is also discussed.
Proceedings ArticleDOI
Feature-Induced Partial Multi-label Learning
TL;DR: In this article, a feature induced partial multi-label learning (fPML) approach is proposed, which simultaneously estimates noisy labels and trains multilabel classifiers by factorizing the observed instance-label association matrix and the instance-feature matrix into low-rank matrices.
Journal ArticleDOI
Probabilistic consensus clustering using evidence accumulation
André Lourenço,Samuel Rota Bulò,Nicola Rebagliati,Ana Fred,Mário A. T. Figueiredo,Marcello Pelillo +5 more
TL;DR: This paper proposes a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix, and proposes an optimization algorithm to find a solution under any double-convex Bregman divergence.
Journal ArticleDOI
Mapping Tumor Hypoxia In Vivo Using Pattern Recognition of Dynamic Contrast-enhanced MRI Data.
Radka Stoyanova,Kris T. Huang,Kiri A. Sandler,HyungJoon Cho,Sean Carlin,Pat Zanzonico,Jason A. Koutcher,Jason A. Koutcher,Ellen Ackerstaff +8 more
TL;DR: This work applies an unsupervised pattern recognition (PR) technique to determine the differential signal versus time curves associated with different tumor microenvironmental characteristics in DCE-MRI data of a preclinical cancer model and assigns tumor compartments/microenvironment to well vascularized, hypoxic, and necrotic areas.
Book ChapterDOI
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference
TL;DR: Blei and Lafferty as discussed by the authors presented stochastic variational inference algorithms for two Bayesian nonnegative matrix factorization (NMF) models, which allow for fast processing of massive datasets.
Related Papers (5)
Learning the parts of objects by non-negative matrix factorization
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Pentti Paatero,Unto Tapper +1 more