
Informationtheoretic Classification Accuracy: A Criterion that Guides Datadriven Combination of Ambiguous Outcome Labels in Multiclass Classification
Outcome labeling ambiguity and subjectivity are ubiquitous in realworld...
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Adversarial Information Bottleneck
The information bottleneck (IB) principle has been adopted to explain de...
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A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space
Recent studies revealed the mathematical connection of deep neural netwo...
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Learnable Graphregularization for Matrix Decomposition
Lowrank approximation models of data matrices have become important mac...
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Tessellated Wasserstein AutoEncoders
Nonadversarial generative models such as variational autoencoder (VAE)...
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Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Matrix decomposition is one of the fundamental tools to discover knowled...
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Towards Understanding Residual and Dilated Dense Neural Networks via Convolutional Sparse Coding
Convolutional neural network (CNN) and its variants have led to many sta...
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Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling
Principal component analysis (PCA) is one of the most widely used dimens...
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Groupsparse SVD Models and Their Applications in Biological Data
Sparse Singular Value Decomposition (SVD) models have been proposed for ...
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Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
Matrix decomposition is a popular and fundamental approach in machine le...
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Sparse Weighted Canonical Correlation Analysis
Given two data matrices X and Y, sparse canonical correlation analysis (...
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Sparse Deep Nonnegative Matrix Factorization
Nonnegative matrix factorization is a powerful technique to realize dime...
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A Unified Joint Matrix Factorization Framework for Data Integration
Nonnegative matrix factorization (NMF) is a powerful tool in data explor...
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Networkregularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
Molecular profiling data (e.g., gene expression) has been used for clini...
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L0norm Sparse Graphregularized SVD for Biclustering
Learning the "blocking" structure is a central challenge for high dimens...
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Shihua Zhang
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