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Dimensional reduction and symplectic reduction
Il Nuovo Cimento B Series 11, 1983The method of dimensional reduction as applied to pure Yang-Mills theory by Manton and Harnadet al. can be related to the symplectic-reduction scheme developed by Marsden and Weinstein when applied to the phase space for a «classical» particle moving in the presence of a Yang-Mills field.
S. Shnider +3 more
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Metric Learning in Dimensionality Reduction
Proceedings of the International Conference on Pattern Recognition Applications and Methods, 2015The emerging big dimensionality in digital domains causes the need of powerful non-linear dimensionality reduction techniques for a rapid and intuitive visual data access. While a couple of powerful non-linear dimensionality reduction tools have been proposed in the last years, their applicability is limited in practice: since a non-linear ...
Schulz, Alexander, Hammer, Barbara
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Bayesian Supervised Dimensionality Reduction
IEEE Transactions on Cybernetics, 2013Dimensionality reduction is commonly used as a preprocessing step before training a supervised learner. However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance. In this paper, we introduce a simple and novel Bayesian supervised dimensionality reduction method that combines linear ...
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Discriminative Dimensionality Reduction Mappings
2012Discriminative dimensionality reduction aims at a low dimensional, usually nonlinear representation of given data such that information as specified by auxiliary discriminative labeling is presented as accurately as possible. This paper centers around two open problems connected to this question: (i) how to evaluate discriminative dimensionality ...
Gisbrecht, Andrej +5 more
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2017
This chapter starts by a principled introduction to principal component analysis (PCA) and ends, hopefully, with strong confidence. We cover an increasingly important method in neuroscience—dimensionality reduction. We discuss the reason this method is important and exemplify it with a popular dimension reduction technique, PCA.
Erik Lee Nylen, Pascal Wallisch
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This chapter starts by a principled introduction to principal component analysis (PCA) and ends, hopefully, with strong confidence. We cover an increasingly important method in neuroscience—dimensionality reduction. We discuss the reason this method is important and exemplify it with a popular dimension reduction technique, PCA.
Erik Lee Nylen, Pascal Wallisch
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Paulo Cáceres Guido +2 more
exaly
The American Cancer Society 2035 challenge goal on cancer mortality reduction
Ca-A Cancer Journal for Clinicians, 2019Jiemin Ma, Ahmedin Jemal, Farhad Islami
exaly
Opportunities and Strategies for Breast Cancer Prevention Through Risk Reduction
Ca-A Cancer Journal for Clinicians, 2008Eleni Linos
exaly