Results 21 to 30 of about 744,201 (267)
Shakespeare and principal components analysis
Digital Scholarship in the Humanities, 2021 Abstract The use of principal components analysis (PCA) in literary studies was pioneered by John Burrows. From him it was adopted by the New Oxford Shakespeare project team, who used it to support their controversial attributions of parts of Arden of Faversham to Shakespeare and parts of the Henry VI trilogy to Marlowe.
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Principal independent component analysis [PDF]
IEEE Transactions on Neural Networks, 1999 Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available.
Jie Luo 0001 +3 more
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Recursive principal components analysis [PDF]
Neural Networks, 2005 A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by
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Principal component analysis [PDF]
, 2014 Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis.
Bro, Rasmus +4 more
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Principal Component Projection Without Principal Component Analysis
CoRR, 2016 We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression.
Roy Frostig +3 more
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Principal components analysis and cyclostationarity
Journal of Multivariate Analysis, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Boudou, Alain, Viguier-Pla, Sylvie
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A principal component analysis for trees
The Annals of Applied Statistics, 2009 The active field of Functional Data Analysis (about understanding the variation in a set of curves) has been recently extended to Object Oriented Data Analysis, which considers populations of more general objects. A particularly challenging extension of this set of ideas is to populations of tree-structured objects.
Aydın, Burcu +4 more
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Generative Principal Component Analysis
CoRR, 2022 ICLR 2022 paper + additional appendix on algorithm-independent lower bounds + corrected experimental results for the Fashion-MNIST ...
Zhaoqiang Liu +4 more
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Cauchy Principal Component Analysis
CoRR, 2014 Principal Component Analysis (PCA) has wide applications in machine learning, text mining and computer vision. Classical PCA based on a Gaussian noise model is fragile to noise of large magnitude. Laplace noise assumption based PCA methods cannot deal with dense noise effectively.
Pengtao Xie, Eric P. Xing
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Pediatric gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are extremely rare and clinically heterogeneous. Management has largely been extrapolated from adult practice. This European Standard Clinical Practice Guideline (ESCP), developed by the EXPeRT network in collaboration with adult NEN experts, provides (adult) evidence ...
Michaela Kuhlen +23 more
wiley +1 more source