
Probabilistic Simplex Component Analysis
This study presents PRISM, a probabilistic simplex component analysis ap...
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Exploring the Subgraph DensitySize Tradeoff via the Lovász Extension
Given an undirected graph, the DensestkSubgraph problem (DkS) seeks to...
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eTREE: Learning Treestructured Embeddings
Matrix factorization (MF) plays an important role in a wide range of mac...
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STELAR: Spatiotemporal Tensor Factorization with Latent Epidemiological Regularization
Accurate prediction of the transmission of epidemic diseases such as COV...
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GAGE: Geometry Preserving Attributed Graph Embeddings
Node representation learning is the task of extracting concise and infor...
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Informationtheoretic Feature Selection via Tensor Decomposition and Submodularity
Feature selection by maximizing highorder mutual information between th...
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TeXGraph: Coupled tensormatrix knowledgegraph embedding for COVID19 drug repurposing
Knowledge graphs (KGs) are powerful tools that codify relational behavio...
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PHASED: PhaseAware SubmodularityBased Energy Disaggregation
Energy disaggregation is the task of discerning the energy consumption o...
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Nonparametric Multivariate Density Estimation: A LowRank Characteristic Function Approach
Effective nonparametric density estimation is a key challenge in highd...
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Mining Large Quasicliques with Quality Guarantees from Vertex Neighborhoods
Mining dense subgraphs is an important primitive across a spectrum of gr...
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GRATE: Granular Recovery of Aggregated Tensor Data by Example
In this paper, we address the challenge of recovering an accurate breakd...
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Generalized Canonical Correlation Analysis: A Subspace Intersection Approach
Generalized Canonical Correlation Analysis (GCCA) is an important tool t...
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Reliable Detection of Unknown CellEdge Users Via Canonical Correlation Analysis
Providing reliable service to users close to the edge between cells rema...
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PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views
Multidimensional data have become ubiquitous and are frequently involved...
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REP: Predicting the TimeCourse of Drug Sensitivity
The biological processes involved in a drug's mechanisms of action are o...
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Nonlinear System Identification via Tensor Completion
Function approximation from input and output data pairs constitutes a fu...
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Machine Learning in the Air
Thanks to the recent advances in processing speed and data acquisition a...
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Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm
We study the problem of learning a mixture model of nonparametric produ...
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Energy Storage Management via Deep QNetworks
Energy storage devices represent environmentally friendly candidates to ...
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Learning Nonlinear Mixtures: Identifiability and Algorithm
Linear mixture models have proven very useful in a plethora of applicati...
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From Gene Expression to Drug Response: A Collaborative Filtering Approach
Predicting the response of cancer cells to drugs is an important problem...
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Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
Joint analysis of data from multiple information repositories facilitate...
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Structured SUMCOR Multiview Canonical Correlation Analysis for LargeScale Data
The sumofcorrelations (SUMCOR) formulation of generalized canonical co...
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Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
Nonnegative matrix factorization (NMF) has become a workhorse for signal...
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MirrorProx SCA Algorithm for Multicast Beamforming and Antenna Selection
This paper considers the (NP)hard problem of joint multicast beamformin...
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Learning Hidden Markov Models from Pairwise Cooccurrences with Applications to Topic Modeling
We present a new algorithm for identifying the transition and emission p...
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Tensors, Learning, and 'Kolmogorov Extension' for Finitealphabet Random Vectors
Estimating the joint probability mass function (PMF) of a set of random ...
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KullbackLeibler Principal Component for Tensors is not NPhard
We study the problem of nonnegative rankone approximation of a nonnegat...
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On Convergence of Epanechnikov Mean Shift
Epanechnikov Mean Shift is a simple yet empirically very effective algor...
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On Identifiability of Nonnegative Matrix Factorization
In this letter, we propose a new identification criterion that guarantee...
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Completing a joint PMF from projections: a lowrank coupled tensor factorization approach
There has recently been considerable interest in completing a lowrank m...
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AnchorFree Correlated Topic Modeling: Identifiability and Algorithm
In topic modeling, many algorithms that guarantee identifiability of the...
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Robust Volume MinimizationBased Matrix Factorization for Remote Sensing and Document Clustering
This paper considers volume minimization (VolMin)based structured matri...
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Tensor Decomposition for Signal Processing and Machine Learning
Tensors or multiway arrays are functions of three or more indices (i,j...
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Scalable and Flexible Multiview MAXVAR Canonical Correlation Analysis
Generalized canonical correlation analysis (GCCA) aims at finding latent...
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Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering
Dimensionality reduction techniques play an essential role in data analy...
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A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
We propose a general algorithmic framework for constrained matrix and te...
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ScoupSMT: Scalable Coupled Sparse MatrixTensor Factorization
How can we correlate neural activity in the human brain as it responds t...
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Nicholas D. Sidiropoulos
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Professor and Chair, Electrical & Computer Engineering at University of Virginia