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We develop a flexible framework for low-rank matrix estimation that allows us to transform noise models into regularization schemes via a simple bootstrap algorithm.
A connection between correlation and contingency
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Auto-association by multilayer perceptrons and singular value decomposition
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Training with noise is equivalent to Tikhonov regularization
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Transformation invariance in pattern recognition: Tangent distance and propagation
Patrice Y Simard, Yann A Le Cun, John S Denker, and Bernard Victorri · 2000
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A generalization of principal component analysis to the exponential family
Michael Collins, Sanjoy Dasgupta, and Robert E. Schapire · 2001
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Unsupervised learning by probabilistic latent semantic analysis
Thomas Hofmann · 2001
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On the distribution of the largest eigenvalue in principal components analysis
Ian Johnstone · 2001
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Variational extensions to EM and multinomial PCA
Wray Buntine · 2002
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Ian Jolliffe · 2002
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On spectral clustering: Analysis and an algorithm
Andrew Y Ng, Michael I Jordan, and Yair Weiss · 2002
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Latent Dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Baselines and bigrams: Simple, good sentiment and topic classification
Sida Wang and Christopher D Manning · 2012
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Unbiased risk estimates for singular value thresholding and spectral estimators
Emmanuel J Candès, Carlos A Sing-Long, and Joshua D Trzasko · 2013
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Scalable convex methods for flexible low-rank matrix modeling
William Fithian and Rahul Mazumder · 2013
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Maxout networks
Ian Goodfellow, David Warde-farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Measures of dependence between random vectors and tests of independence. Literature review
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Principal components analysis corrects for stratification in genome-wide association studies
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The adaptive LASSO and its oracle properties
H. Zou · 2006
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Sparse principal component analysis
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Correspondence Analysis in Practice, Second Edition
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Capturing heterogeneity in gene expression studies by surrogate variable analysis
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Reconstruction of a low-rank matrix in the presence of Gaussian noise
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Constrained Principal Component Analysis and Related Techniques
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Learning with marginalized corrupted features
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Regularised PCA to denoise and visualise data
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Dropout training as adaptive regularization
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Feature noising for log-linear structured prediction
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The dropout learning algorithm
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