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Found 43 papers, 6 papers with code
Revisiting Deep Generalized Canonical Correlation Analysis
Others overload the problem by also seeking to reveal what is not common among the views -- i. e., the private components that are needed to fully reconstruct each view.
On High-dimensional and Low-rank Tensor Bandits
To address this limitation, this work studies a general tensor bandits model, where actions and system parameters are represented by tensors as opposed to vectors, and we particularly focus on the case that the unknown system tensor is low-rank.
Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis
Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal.
Minimizing low-rank models of high-order tensors: Hardness, span, tight relaxation, and applications
We show that this fundamental tensor problem is NP-hard for any tensor rank higher than one, and polynomial-time solvable in the rank-one case.
Learning Multivariate CDFs and Copulas using Tensor Factorization
Learning the multivariate distribution of data is a core challenge in statistics and machine learning.
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation
Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality.
Probabilistic Simplex Component Analysis
PRISM uses a simple probabilistic model, namely, uniform simplex data distribution and additive Gaussian noise, and it carries out inference by maximum likelihood.
eTREE: Learning Tree-structured Embeddings
In many applications, the categories of items exhibit a hierarchical tree structure.
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures.
GAGE: Geometry Preserving Attributed Graph Embeddings
Various real-world networks include information about both node connectivity and certain node attributes, in the form of features or time-series data.
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity
By indirectly aiming to predict the latent variable of the naive Bayes model instead of the original target variable, it is possible to formulate the feature selection problem as maximization of a monotone submodular function subject to a cardinality constraint - which can be tackled using a greedy algorithm that comes with performance guarantees.
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing
Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases.
PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing energy usage.
Low-rank Characteristic Tensor Density Estimation Part I: Foundations
Any multivariate density can be represented by its characteristic function, via the Fourier transform.
Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods
In this work, we formally establish that two recurring characteristics of real-world graphs, namely heavy-tailed degree distributions and large clustering coefficients, imply the existence of substantially large vertex neighborhoods with high edge-density.
GRATE: Granular Recovery of Aggregated Tensor Data by Example
In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples.
Generalized Canonical Correlation Analysis: A Subspace Intersection Approach
It is shown that from a linear algebra point of view, GCCA is tantamount to subspace intersection; and conditions under which the common subspace of the different views is identifiable are provided.
PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views
The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions.
REP: Predicting the Time-Course of Drug Sensitivity
Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug.
Nonlinear System Identification via Tensor Completion
Deep neural networks are currently the most popular method for learning to mimic the input-output relationship of a general nonlinear system, as they have proven to be very effective in approximating complex highly nonlinear functions.
Machine Learning in the Air
Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner.
Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm
We study the problem of learning a mixture model of non-parametric product distributions.
Energy Storage Management via Deep Q-Networks
Energy storage devices represent environmentally friendly candidates to cope with volatile renewable energy generation.
Learning Nonlinear Mixtures: Identifiability and Algorithm
Linear mixture models have proven very useful in a plethora of applications, e. g., topic modeling, clustering, and source separation.
From Gene Expression to Drug Response: A Collaborative Filtering Approach
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics.
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
The resulting community detection approach is successful even when some links in the graphs are missing.
Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data
In this work, we propose a new computational framework for large-scale SUMCOR GCCA that can easily incorporate a suite of structural regularizers which are frequently used in data analytics.
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
Third, the majority of the existing methods assume that there are known (or easily estimated) degradation operators applied to the SRI to form the corresponding HSI and MSI--which is hardly the case in practice.
Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
Perhaps a bit surprisingly, the understanding to its model identifiability---the major reason behind the interpretability in many applications such as topic mining and hyperspectral imaging---had been rather limited until recent years.
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data.
Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors
This paper shows, perhaps surprisingly, that if the joint PMF of any three variables can be estimated, then the joint PMF of all the variables can be provably recovered under relatively mild conditions.
Kullback-Leibler Principal Component for Tensors is not NP-hard
We study the problem of nonnegative rank-one approximation of a nonnegative tensor, and show that the globally optimal solution that minimizes the generalized Kullback-Leibler divergence can be efficiently obtained, i. e., it is not NP-hard.
On Convergence of Epanechnikov Mean Shift
However, since the procedure involves non-smooth kernel density functions, the convergence behavior of Epanechnikov mean shift lacks theoretical support as of this writing---most of the existing analyses are based on smooth functions and thus cannot be applied to Epanechnikov Mean Shift.
On Identifiability of Nonnegative Matrix Factorization
In this letter, we propose a new identification criterion that guarantees the recovery of the low-rank latent factors in the nonnegative matrix factorization (NMF) model, under mild conditions.
Completing a joint PMF from projections: a low-rank coupled tensor factorization approach
There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries.
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i. e., words that only appear (with positive probability) in one topic.
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN).
Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering
This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF).
Tensor Decomposition for Signal Processing and Machine Learning
Tensors or {\em multi-way arrays} are functions of three or more indices $(i, j, k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r, c)$ for (row, column).
Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis
Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities.
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering
Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification.
Joint Tensor Factorization and Outlying Slab Suppression with Applications
Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants.
A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning.
