A new model relaxes PARAFAC2 for nonnegative constraints on varying modes.
problem Handling variability in tensor decomposition for source separation.
method Introducing a flexible PARAFAC2 model with nonnegativity constraints on varying modes.
result An algorithm for computing the flexible PARAFAC2 model is derived and validated.
ZNMF improves facial recognition performance using data-dependent penalties.
problem Facial recognition performance in the Cambridge ORL database.
method ZNMF uses data-dependent auxiliary constraints to modify NMF.
result ZNMF outperforms other constrained NMF algorithms in facial recognition.
The paper introduces MU for NMF with β-divergences and disjoint constraints.
problem Nonnegative matrix factorization with constraints.
method Design multiplicative updates for NMF based on β-divergences with disjoint constraints. result Multiplicative updates satisfy constraints and decrease the objective function.
An algorithm simplifies optimization with nonnegative and orthogonal constraints.
problem Optimization problems with nonnegative and orthogonal constraints.
method Support-set algorithm exploiting structural sparsity.
result Global convergence to first-order stationary point with iteration complexity O(ε−2). We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into…
New NMF algorithm uses Toeplitz matrix for facial recognition.
problem Facial recognition performance improvement.
method Proposes TNMF algorithm with Toeplitz penalty for NMF.
result TNMF outperforms ZNMF and other constrained NMF algorithms.
BALSON optimizes parameters with Bayesian approach and Dirichlet distribution.
problem Data fitting with nonnegative L1-norm constraints.
method Bayesian approach, Gaussian likelihood, Dirichlet distribution, sampling methods.
result BALSON outperforms conventional methods in polynomial fitting.
In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive pro…
For certain manifolds, nonnegative Ricci curvature limits dimension and forces almost abelian fundamental group.
problem Bounding the dimension of manifolds with nonnegative Ricci curvature and specific fundamental group properties.
method Dimensional estimates for RCD(0,N) spaces with large Hausdorff dimension. result If dimension is less than 12, the fundamental group is almost abelian.
Enforces physical constraints in GP regression models.
problem Unbounded GP models can produce infeasible values.
method Enforces nonnegativity constraints probabilistically.
result Reduces model variance and enforces physical bounds.
Study shows curvature constraints force submanifolds to have specific topology or geometry.
problem Curvature constraints on submanifolds in nonnegative curvature spaces.
method Investigates submanifolds with lower bounds on sectional curvature and mean curvature.
result Curvature constraints force submanifolds to have specific topology or geometry.
Improved NMF using variance-reduced MU rule.
problem Slow convergence of multiplicative update in NMF.
method Introduces variance-reduced stochastic multiplicative update.
result Robustly outperforms state-of-the-art algorithms.
Motivated by the problem of optimal portfolio liquidation under transient price impact, we study the minimization of energy functionals with completely monotone displacement kernel under an integral constraint. The corresponding minimizers can be characterized by Fredholm integral equations of the second type with cons…
Bayesian NMF model improves predictions and avoids overfitting.
problem Predicting missing values and finding hidden patterns in nonnegative data.
method Flexible and hierarchical prior for Bayesian NMF with Gibbs sampling.
result The proposed model leads to better predictions and avoids overfitting.
This paper introduces a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. This new model not only generalizes the commonly used linear mixing model, but also allows for possible nonlinear effects to be easily handled, relying on mild assumptions regarding…
Paper introduces a novel matrix-wise sparse MNNLS formulation and algorithm.
problem Sparse nonnegative least squares with multiple right-hand sides.
method Matrix-wise sparsity constraint, two-step algorithm.
result More accurate results compared to state-of-the-art methods.
Stacked regressions improve predictive accuracy by combining estimators.
problem Improve predictive accuracy in regression models.
method Analogous to least-squares, learn combination weights by minimizing regularized empirical risk with nonnegativity constraint.
result The stacked estimator has strictly smaller population risk than the best single estimator, especially when signal-to-noise ratio is small.
New method quantifies multivariate redundancy using maximum entropy decompositions.
problem Elusive multivariate measures of redundancy that comply with nonnegativity and axioms.
method Maximum entropy framework, rooted tree-based decompositions of mutual information.
result Quantifies different multivariate redundancy contributions.
Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices. In this paper we propose a Frank-Wolfe (FW) solver to optimize the symmetric nonnegative matrix factorization problem under a simplicial constraint, whic…
A new algorithm solves nonnegative least squares faster with nonnegative data.
problem Nonnegative least squares problems with nonnegative data.
method Primal-dual perspective accelerated algorithm with adaptive restart.
result Oracle complexity independent of matrix constants, solvable to multiplicative error.
The paper tackles tensor factorization and completion from noisy data.
problem Sparse nonnegative tensor factorization and completion from partial and noisy observations.
method Minimizes the sum of maximum likelihood estimation and tensor ℓ0 norm with nonnegativity constraints. result Error bounds and minimax lower bounds are established for the proposed model.
New algorithm learns interpretable CP-basis from streaming tensor data under Markovian constraints.
problem Learning interpretable CP-basis from streaming tensor data under Markovian constraints.
method Online Tensor Factorization (OTF) with CANDECOMP/PARAFAC (CP) decomposition, proving convergence to stationary points.
result Algorithm converges almost surely to stationary points of the objective function under Markovian data generation.
Efficient algorithm for analyzing compositional data.
problem Compositional data analysis with nonnegative values summing to one.
method Proposes an efficient solution path algorithm for l1 regularized regression with compositional data. result The proposed algorithm is faster than existing methods, especially in high-dimensional cases.
Enhances tensor regression for interpretability and performance.
problem Interpreting and modeling multidimensional tensor data with structural heterogeneity.
method Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR) with hybrid regularization and nonnegativity constraints.
result NS-KTR outperforms conventional methods in synthetic and real hyperspectral datasets.
In curved spaces, isoperimetric sets don't exist for small volumes.
problem Nonexistence of isoperimetric sets in spaces of positive curvature.
method Constructing specific noncompact smooth Riemannian manifolds with positive curvature.
result Nonexistence of isoperimetric sets for small volumes in spaces of positive curvature.
Improved Bayesian quadrature for constrained functions.
problem Performing inference of constrained functions in Bayesian inference.
method Bayesian framework with explicit approximation schemes for constraints, log transformation for high dynamic range, and optimization of hyperparameters in original space.
result Model achieves superior estimates using less time than existing procedures.
Paper develops compact formulations for optimization problems with rank-one convex functions and indicator variables.
problem Optimization problems involving rank-one convex functions with support constraints.
method Perspective reformulation techniques to exploit conic structure and establish convex hull results.
result Systematic perspective formulations for convex hull descriptions of sets with nonlinear separable or non-separable objective functions and combinatorial constraints.
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedding. We further propose to iterate the two steps in LLE repeatedly to improve the results. Thirdly, we relax the kNN constraint of LLE and p…
The paper explores curvature constraints on Kodaira dimension for specific almost Hermitian manifolds.
problem Investigating Riemannian curvature constraints on the Kodaira dimension of compact almost Hermitian manifolds.
method Analyzing compact almost Hermitian manifolds in the Gray-Hervella class and Hermitian manifolds with nonnegative scalar curvature.
result For compact almost Hermitian manifolds with nonnegative scalar curvature, the Kodaira dimension is either -∞ or 0, with specific conditions.
This paper describes a new approach, based on linear programming, for computing nonnegative matrix factorizations (NMFs). The key idea is a data-driven model for the factorization where the most salient features in the data are used to express the remaining features. More precisely, given a data matrix X, the algorithm…
Method improves clarity in forecasting spatio-temporal data.
problem Forecasting spatio-temporal data with clarity and interpretability.
method Supervised semi-nonnegative matrix factorization with frequency regularization.
result Method offers clearer interpretability in forecasting spatio-temporal data.
Inexact Riemannian optimization converges to stationary points efficiently.
problem Analyzing convergence and complexity of inexact Riemannian optimization.
method Tangential Block Majorization-Minimization (tBMM) framework.
result tBMM converges to an ε-stationary point within O(ε⁻²) iterations.
Study critical quasilinear equations on Riemannian manifolds with curvature constraints.
problem Investigate critical quasilinear elliptic equations on Riemannian manifolds with nonnegative Ricci curvature.
method Utilize a new nonlinear Kato inequality and Cheng-Yau type gradient estimates for positive solutions.
result Classify positive solutions to the critical p-Laplace equation and show rigidity concerning the ambient manifold. Paper finds unique viscosity solution to complex control problems.
problem Complex stochastic control problems with singular terminal state constraints.
method Establishes existence of unique nonnegative continuous viscosity solution using novel comparison principle.
result Unique viscosity solution to HJB equation for linear-quadratic control problems.
Unified Bayesian NMF models for binary data with automatic dimension selection.
problem Binary data analysis with nonnegative matrix factorization and link functions.
method Bayesian mean-parameterized nonnegative binary matrix factorization (NBMF) models with collapsed Gibbs and variational algorithms.
result Automatic detection of relevant components without manual tuning.
Proposes a method to learn optimal neighbors and projection matrix in low-dimensional space.
problem Difficulty in precisely measuring similarity and selecting optimal neighbors in high-dimensional space.
method Models similarity and neighbors as variables, optimizing a unified objective function with nonnegative and sum-to-one constraints.
result Optimal similarity and projection matrix learned simultaneously, with adaptive regularization parameter.
Proposes CRG_IMSC for better clustering of multi-view data.
problem Lack of effective connectivity in clustering results.
method Directly obtains clustering result with nonnegative constraint; constructs connectivity matrix based on spectral clustering result; uses multiplicative update algorithm.
result Improves clustering performance on benchmark datasets.
Paper reviews and compares NMF, PLSA, LBA, EMA, and LCA models.
problem Identifiability of latent models.
method Comparison and proof of identifiability.
result Identifiability of LBA, EMA, LCA, PLSA is unique if and only if NMF is unique.
A new method uses literature constraints to improve phenotyping from EHR data.
problem Improving phenotyping from electronic health records (EHR) data.
method Constrained tensor factorization with literature constraints.
result Improved phenotyping results for hypertensive patients.
We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers. We prove that the se…
Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF …
Study shows topological constraints on manifolds with non-negative scalar curvature and mean convex boundary.
problem Topological constraints on manifolds with non-negative scalar curvature and mean convex boundary.
method Constructing examples of compact manifolds that do not admit such metrics.
result Many compact manifolds with boundary do not admit a metric of non-negative scalar curvature and mean convex boundary.
The numeraire portfolio in a financial market is the unique positive wealth process that makes all other nonnegative wealth processes, when deflated by it, supermartingales. The numeraire portfolio depends on market characteristics, which include: (a) the information flow available to acting agents, given by a filtrati…
Study optimal consumption and portfolio strategies with no-borrowing constraint in financial markets.
problem Maximizing utility from consumption under constraints in a stochastic environment.
method Lagrange duality and singular control problem to solve dynamic no-borrowing constraint.
result Retrieve optimal portfolio and consumption plans via dual singular control problem.
Derives matrix Harnack inequalities for semilinear heat equations on manifolds.
problem Bounding solutions of semilinear heat equations on manifolds with geometric constraints.
method Applies Li-Yau estimates to derive Harnack inequalities for positive solutions.
result Derives matrix Harnack inequalities for positive solutions of semilinear heat equations.
State spaces of multifactor approximations of nonnegative Volterra processes are linear transformations of the nonnegative orthant.
problem Characterizing state spaces of multifactor approximations of nonnegative Volterra processes.
method Explicit linear transformation of the nonnegative orthant.
result State spaces of multifactor approximations of nonnegative Volterra processes are given by explicit linear transformation of the nonnegative orthant.
Fixed points of nonnegative neural networks are analyzed using fixed point theory.
problem Analyzing fixed points in nonnegative neural networks.
method Fixed point theory, nonlinear Perron-Frobenius theory, monotonic and scalable mappings.
result Conditions for the existence of fixed points in nonnegative neural networks are provided.
Study Euler characteristic of manifolds with almost nonnegative curvature operator, showing nonnegativity under certain conditions.
problem Addressing the sign of Euler characteristic for manifolds with almost nonnegative curvature operator.
method Analyzing closed manifolds with uniform upper bounds on curvature operator and applying ANCO-type conditions.
result Nonnegative Euler characteristic for closed 2n-dimensional manifolds with almost nonnegative curvature operator and uniform upper bounds on curvature.