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Cross-Validation for Unsupervised Learning
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Cross-validation (CV) is a popular method for model-selection. Unfortunately, it is not immediately obvious how to apply CV to unsupervised or exploratory contexts. This thesis discusses some extensions of cross-validation to unsupervised learning, specifically focusing on the problem of choosing how many principal components to keep. We introduce the latent factor model, define an objective criterion, and show how CV can be used to estimate the intrinsic dimensionality of a data set. Through both simulation and theory, we demonstrate that cross-validation is a valuable tool for unsupervised learning.
Forward citations
Cited by 2 Pith papers
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Bi-cross validation for estimating spectral clustering hyper parameters
A method using bi-cross validation on the inverted Laplacian matrix to estimate spectral clustering hyperparameters, demonstrated on simulations and LCLS data, but lacking a proof and failing on one synthetic case.
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Fusing heterogeneous data sets
The thesis proposes logistic PCA via non-convex singular value thresholding, generalized SCA for binary and quantitative data, and P-ESCA for multiple mixed-type data sets with structured sparsity to separate common a...
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