Proposes FARM model combining latent factor and sparse regression.
problem Testing adequacy of latent factor and sparse regression models.
method Factor Augmented sparse linear Regression Model (FARM) with FabTest and ANOVA type tests.
result Model robustness and effectiveness validated through experiments.
Sparse APCA identifies sparse factors in financial returns over time.
problem Analyzing co-movements of high-dimensional panel data over time.
method Sparse asymptotic PCA with truncated power method for sparse factors and sequential deflation for multi-factor cases.
result Identification of nine risk factors influencing the S&P 500 stock market.
Sparse VAE learns latent factors from high-dimensional data.
problem Unsupervised representation learning on high-dimensional data.
method Sparse VAE model that learns latent factors summarizing data associations.
result Sparse VAE can recover true model parameters with infinite data.
Bayesian model infers factor dimensionality and sparse loading matrix adaptively.
problem Inference of high-dimensional sparse factor model with varying sparsity and factor dimensions.
method Adaptive Bayesian sparse factor model with posterior concentration.
result Posterior distribution asymptotically concentrates on true factor dimensionality and sparsity.
New method for hyperparameter tuning in sparse matrix factorization.
problem Hyperparameter tuning in sparse matrix factorization.
method Numerical method based on evaluating the zero point of normalization factor in sparse matrix prior.
result Our method outperforms existing algorithms in ground-truth sparse matrix reconstruction.
EFS uses LLMs to optimize sparse portfolios by evolving alpha factors.
problem Sparse portfolio optimization in dynamic market regimes.
method Evolutionary feedback loop with LLM-generated alpha factors.
result Significantly outperforms baselines in diverse datasets.
The paper explains practical insights for sparse network modeling.
problem Resolving pathologies in traditional network modeling, focusing on sparsity.
method Sparse exchangeable graphs, network subsampling, test-train dataset splitting, mean field variational inference.
result Practical insights and methods for sparse network modeling.
Scalable psFA for fMRI data extracts sparse components.
problem Extracting neural representations from fMRI data with probabilistic formulation.
method Group level scalable probabilistic sparse factor analysis (psFA) with spatial sparsity, component pruning, and heteroscedastic noise modeling.
result Sparse components similar to group ICA and reduced noise in activated areas.
Sparse GFA identifies disease factors in FTD subgroups.
problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.
This work tackles sparse coding in DLRA for interpretable multiway data.
problem Sparse coding in DLRA for interpretable multiway data.
method Proposes a new sparse-coding subproblem (MSC) and several algorithms to solve it.
result DLRA extends low-rank approximations, reducing variance and enhancing interpretability.
A new NMF variant tackles underdetermined problems with sparse and separable assumptions.
problem Underdetermined blind source separation, especially multispectral image unmixing.
method Sparse Separable Nonnegative Matrix Factorization (SSNMF) combining separability and sparsity assumptions. Algorithm based on SNPA and sparse nonnegative least squares.
result In noiseless settings, the algorithm recovers true underlying sources.
Global optimization algorithm finds sparse mixed membership matrix factorization's global optimum.
problem Sparse mixed membership matrix factorization problems with local optima.
method Derives a global optimization algorithm for sparse mixed membership matrix factorization.
result Guaranteed ε-global optimum across random initializations and multiple modes. New method for disentangling latent factors with sparse dependencies.
problem Disentangling latent factors from observed variables and past factors.
method Mechanism sparsity regularization and sparse causal graphical model.
result Identifiability of latent factors up to a sparse causal graph.
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.
Framework captures missing data in sparse data sets.
problem Capturing missing data in extremely sparse data sets.
method Coupled compound Poisson factorization with stochastic variational inference.
result Explicitly modeling missing data improves results in clustering, prediction, and matrix factorization.
The article develops a method to learn sparse and low rank PARAFAC decomposition robust to noise.
problem Learning sparse and low rank PARAFAC decomposition for tensors with missing values.
method Bayesian model with elastic net regularization, efficient algorithms for large scale problems.
result The method finds true rank and sparse factor matrix robust to noise.
Algorithm for factorizing financial network data into groups.
problem Modeling groups within heterogeneous financial networks.
method Non-negative factorization of an occurrence tensor, l0 norm for sparsity, efficient splitting method.
result Effective factorization of financial documents into embedded groups.
This study improves fast non-Bayesian Poisson factorization for implicit-feedback recommendation systems.
problem Improving recommendation quality and speed for implicit-feedback data.
method Regularized Poisson models, frequentist optimization, sparse solutions.
result Frequentist approach yields better top-N recommendations with shorter fitting times.
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
problem Sparse and nonstationary movement speed data from urban roads.
method Nonstationary Temporal Matrix Factorization (NoTMF) model.
result NoTMF outperforms baseline models in forecasting urban road movement speeds.
Paper proposes a new method for sparse covariance Cholesky factor estimation.
problem Estimating sparse covariance matrices for ordered data.
method Matrix loss penalization approach for sparse Cholesky factor estimation.
result The proposed method outperforms existing regression-based approaches in simulations and real data.
XCAN uses cross-product penalization for sparse matrix factorization.
problem Understanding complex data structures.
method Sparse matrix factorization with a loss function balancing variance and structural preservation.
result Flexible modeling approach for diverse applications.
New method for sparse data using L1-NMF with improved sparsity control.
problem Sparse data with false zeros and heavy-tailed noise.
method Component-wise L1-NMF with weighted penalization and coordinate descent.
result Effective in handling sparse data with false zeros.
While matrix factorisation models are ubiquitous in large scale recommendation and search, real time application of such models requires inner product computations over an intractably large set of item factors. In this manuscript we present a novel framework that uses the inverted index representation to exploit struct…
Method learns shared and specific factors in multi-study gene expression data.
problem Understanding shared and specific factors in high-dimensional multi-study data.
method Nonlinear multi-study factor model with sparse variational autoencoder.
result Method recovers meaningful shared and specific factors in platelet gene expression data.
New principle for disentangling latent factors using sparse regularization.
problem Disentangling latent factors from complex data.
method Sparse regularization of latent mechanisms to induce disentanglement.
result Recovery of latent variables up to permutation under certain conditions.
Deep weight factorization improves neural network training through smooth optimization of sparse penalties.
problem Challenges in applying sparse regularization in neural networks due to non-differentiability of penalties.
method Introduces deep weight factorization, decomposing weights into multiple factors for smooth optimization of L1-penalized networks. result Deep weight factorization outperforms shallow factorization and pruning methods consistently across various architectures and datasets.
A combined model integrates latent factor and logistic regression for citation network analysis.
problem Insufficient representation by either latent factor or logistic regression alone.
method Proposes a combined model integrating latent factor and logistic regression, with parameter estimation through joint-likelihood and penalty terms.
result The proposed method captures both main technological trends and ad-hoc dependencies in citation networks.
LOVE method estimates factor model entries and structure from sparse data.
problem Estimating factor model entries and structure from sparse data with unknown number of factors and pure variables.
method Adaptive estimation method LOVE that identifies loading matrix A from X = AZ + E.
result The loading matrix A is uniquely defined up to signed permutations with minimal conditions.
The paper analyzes noisy IMC under sparse factor models and provides theoretical error bounds.
problem Noisy matrix completion with sparse factor models.
method Extends Soni's theorem to sparsity-regularized maximum likelihood estimators for Gaussian noise.
result Error bounds for noisy IMC under sparse factor models.
We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions. This problem can be viewed as a simplification of the deep learning problem where finding a factorization corresponds to finding edges in different layers and valu…
Proposes a method for tensor completion with sparse factors and missing data.
problem Recovering nonnegative data from noisy observations with missing values.
method Sparse nonnegative Tucker decomposition with ℓ0 norm for sparsity, maximum likelihood estimation, and error bounds. result The method outperforms existing tensor-based or matrix-based methods in nonnegative tensor data completion.
FMs predict student knowledge accurately and efficiently, even with sparse data.
problem Predicting student knowledge progression over time.
method Factorization Machines (FMs) as a regression or classification model.
result FMs can accurately estimate student knowledge, even with sparse data.
Proposes a nonparametric tensor factorization for sparse data.
problem Handling sparse tensor data with structural and interpretability benefits.
method Hierarchical Gamma processes and Poisson random measures for tensor-valued process, Dirichlet processes for sampling entry indices, Gaussian processes for values.
result Demonstrates superior performance on benchmark datasets.
Divide-and-conquer method speeds sparse factorization for large matrices.
problem Sparse factorization of large matrices for statistical learning.
method Statistical problem formulation, divide-and-conquer approach, stagewise learning.
result Efficient algorithm with lower complexity than existing methods.
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We…
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set, adapting it to specific data. V…
Paper proposes VAE-BPTF for better tensor factorization of sparse, imbalanced count data.
problem Inference of Bayesian Poisson-Gamma models for sparse and imbalanced count data is challenging.
method Variational auto-encoder framework with multi-layer perceptron networks for complex update information sharing and reweighting.
result VAE-BPTF outperforms current models in reconstruction errors and latent factor coherence across real-world datasets.
A new covariance estimator reduces dimensionality and improves portfolio forecasting.
problem Estimating high-dimensional covariance matrices with weak factors.
method Sparse Approximate Factor (SAF) model with l1-regularization. result SAF estimator outperforms other methods in portfolio forecasting.
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to …
A new method STMF improves missing value prediction using tropical semiring.
problem Limited capability of linear models to model complex relations.
method Sparse Tropical Matrix Factorization (STMF) using tropical semiring.
result STMF outperforms NMF on real data, especially in handling extreme values.
New nonconvex regularizers improve low-rank matrix recovery efficiency and accuracy.
problem Efficiently recover low-rank matrices from incomplete data.
method Factor group-sparse regularization, related to Schatten-p norms.
result Improved generalization error bounds for Schatten-p norms as p decreases.
We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the applic…
We consider the problem of sparse estimation in a factor analysis model. A traditional estimation procedure in use is the following two-step approach: the model is estimated by maximum likelihood method and then a rotation technique is utilized to find sparse factor loadings. However, the maximum likelihood estimates c…
Study confirms sparse coding in whole brain using MRI data.
problem Sparse coding in the whole brain's neural activities.
method Applied various matrix factorization methods to fMRI data.
result Sparse coding hypothesis in information representation in the whole human brain is confirmed.
Study tensor completion for tensors with sparse CP factors under noisy conditions.
problem Noisy tensor completion for tensors with a sparse CP factor.
method Complexity-regularized maximum likelihood principle and ADMM-type algorithm.
result General theoretical error bounds and validation via experiments.
Dynamic risk factor model improves portfolio performance in high dimensions.
problem Dynamic portfolio allocation in high-dimensional financial markets.
method Time-varying sparsity on factor loadings, sequential learning of parameters and volatilities.
result Significant portfolio performance improvements and higher utility gains.
SOFAR learns large-scale association networks efficiently.
problem Efficiently understanding large-scale response-predictor association networks.
method Sparse Orthogonal Factor Regression (SOFAR) via sparse singular value decomposition with orthogonality constraints.
result SOFAR achieves statistical efficiency and scientific insights.
spex-LVM infers interpretable latent factors from biomedical data.
problem Inability to learn sparse and interpretable hidden states.
method Factorial latent variable model with sparse priors and domain-relevant annotations.
result Robustly identifies relevant structure in RNA-seq datasets.