
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
Principal component analysis (PCA) is a classical and ubiquitous method ...
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Selecting the number of components in PCA via random signflips
Dimensionality reduction via PCA and factor analysis is an important too...
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Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion
Commercial building heating, ventilation, and air conditioning (HVAC) sy...
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Stochastic Gradients for LargeScale Tensor Decomposition
Tensor decomposition is a wellknown tool for multiway data analysis. Th...
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Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised...
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Optimally Weighted PCA for HighDimensional Heteroscedastic Data
Modern applications increasingly involve highdimensional and heterogene...
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Generalized Canonical Polyadic Tensor Decomposition
Tensor decomposition is a fundamental unsupervised machine learning meth...
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Instant Accident Reporting and Crowdsensed Road Condition Analytics for Smart Cities
The following report contains information about a proposed technology by...
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Subspace Clustering using Ensembles of KSubspaces
We present a novel approach to the subspace clustering problem that leve...
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Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace...
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David Hong
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