Principal component analysis (PCA) for binary data, known as logistic PCA, has become a popular alternative to dimensionality reduction of binary data. It is motivated as an extension of ordinary PCA by means of a matrix factorization, akin to the singular value decomposition, that maximizes the Bernoulli log-likelihoo…
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48 results for “logistic matrix factorization”
Paper finds a lower bound for estimating low-rank matrices in logistic regression.
problem Estimating low-rank coefficient matrices in logistic regression.
method Derives a minimax lower bound on the risk.
result The bound depends on matrix dimensions, rank, and sample size.
A new method combines MCMC and VI using contrastive divergence.
problem Improving variational inference by incorporating MCMC steps.
method Introducing variational contrastive divergence (VCD) to optimize variational parameters.
result Optimizing VCD leads to better predictive performance in latent variable models.
Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem classes, such as linear or logistic regression, several theoretical studies have demonstrated the equivalence between dropout and a fully de…
The paper improves matrix completion with auxiliary covariates using LS estimation.
problem Matrix completion with noisy data and auxiliary covariates.
method Iterative least squares estimation with statistical properties derived.
result Asymptotic normal distributions of estimators for low-rank matrix and coefficient matrix.
Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms or cell lines. Therefore, computational prediction methods are needed to address these is…
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.
Estimates future ICU demand using age-structured data and logistic modeling.
problem Projecting future ICU demand from low-quality data.
method Age-structured sampling, logistic fit, subnotification factor correction.
result Corrected logistic curve projects future ICU demand.
Improved sketching for logistic and regression with near-linear dimensions.
problem Efficiently approximate and logistic regression problems.
method New sketching techniques achieving near-linear dimensions for both problems.
result Achieved near-linear sketching dimensions for and logistic regression.
Joint Nuclear and Regularization for Logistic Matrix Regression with Applications to Brain Imagingstat.ME
New method uses nuclear and ℓ1 penalties for matrix regression, improving brain disorder detection.
problem Modeling high-dimensional matrix predictors with binary responses.
method Convex optimization with ADMM for low-rank and sparse structures.
result Effective in identifying brain disorder-related connectivity patterns.
Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvemen…
A new hybrid Newton algorithm improves convergence in logistic regression.
problem Solving large-scale binary classification problems efficiently.
method Proposes a hybrid stochastic Newton algorithm with two weighted components in the Hessian matrix estimation.
result Proves almost sure convergence to the true parameter of logistic regression.
TASTE combines static and temporal data for phenotyping EHRs.
problem Phenotyping EHRs with both static and temporal data.
method Jointly models static and temporal tensors using PARAFAC2 and non-negative matrix factorization, alternatingly solving sub-problems.
result TASTE outperforms existing methods in speed and clinical meaningfulness of phenotypes.
A new algorithm reduces the time and space complexity for multinomial logistic bandits.
problem High-dimensional feedback in multinomial logistic bandits makes existing algorithms inefficient.
method Integrates frequent directions matrix sketching into OFUL-MLogB to reduce time and space complexity.
result Achieves a regret bound of .
In this paper, we study the general problem of optimizing a convex function over the set of matrices, subject to rank constraints on . However, existing first-order methods for solving such problems either are too slow to converge, or require multiple invocations of singular value decompositions.…
Novel bounds for logistic regression coreset construction and feature selection.
problem Efficiently summarize and reduce logistic regression inputs.
method Feature space sketching for logistic regression.
result Tight bounds for coreset construction and feature selection.
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversionstat.ML
Improved regret bounds for logistic bandits via novel confidence set construction.
problem Dependencies in parameter space for logistic bandits, especially when .
method Regret-to-confidence-set conversion (R2CS) to construct convex confidence sets.
result Strict improvement in regret bound w.r.t. in logistic bandits.
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the…
Logistic regression is used thousands of times a day to fit data, predict future outcomes, and assess the statistical significance of explanatory variables. When used for the purpose of statistical inference, logistic models produce p-values for the regression coefficients by using an approximation to the distribution …
The paper identifies universal features for high-dimensional data inference.
problem Identifying universal low-dimensional features from high-dimensional data for inference tasks.
method Introduces natural notions of universality and shows a local equivalence among them, using information geometry.
result Reveals the complementary roles of various data analysis techniques.
We consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices. The generative model is formulated as a matrix logistic regression. The performance of the model is analysed in a high-dimensional regime under a structural assumption. The mi…
Develops first optimal algorithm for logistic bandits.
problem Pure exploration in logistic bandits.
method Logistic track-and-stop (Log-TS) algorithm.
result Asymptotically matches lower bound for expected sample complexity.
Study compares forecasting methods for logistics time series.
problem Improving forecasting accuracy in logistics.
method Compared statistical and machine learning methods on simulated time series.
result Statistical methods outperformed machine learning in one-step forecasts.
Efficiently preserves privacy in logistic regression for IoT data.
problem Balancing data privacy and utility in collaborative learning.
method Matrix encryption approach for secure multi-party computation.
result Proposes a privacy-preserving logistic regression model with fast convergence.
Study integrates attentional and spacing factors to improve category learning models.
problem Understanding the impact of training sequences on category learning.
method Introduced a novel integration of attentional factors and spacing into logistic knowledge tracing models.
result Enhanced model predicts students' learning outcomes better than existing models.
A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each. This paper observes that if the inference problem is "smoothed" through the addition of entropy terms, for fixed messages, the learning ob…
A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programmingstat.ML
Improves logistic regression performance with nonconvex programming.
problem Stochastic generalized linear regression with chance constraints.
method Nonconvex programming techniques, clustering, quantile estimation.
result Over 1 to 2 percent improvement in model performance.
Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problemscs.LG
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost…
Improved regret bound for MNL MDPs with variance-aware approach.
problem Optimal reinforcement learning for MNL MDPs with structured variance.
method Introducing a problem-dependent constant measuring average variance, proposing an algorithm with improved regret bound.
result Minimax optimal regret bound of for structured MDPs.
Method completes mixed matrix from complex surveys with heterogeneous missingness.
problem Recovering a mixed dataframe matrix from complex survey sampling with different missingness patterns.
method Two-stage procedure: logistic regression for missingness modeling, and weighted log-likelihood maximization with low-rank constraint.
result The proposed method achieves sublinear convergence and shows superior performance compared to existing methods.
A new algorithm approximates logistic regression probabilities efficiently.
problem Efficiently approximating probabilities in logistic regression for large datasets.
method Randomized sampling-based algorithm with leverage scores.
result Accurate approximations to estimated probabilities with smaller sample sizes.
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.
Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logisti…
Gradient descent proves global convergence for 4-layer matrix factorization.
problem Global convergence of gradient descent on four-layer matrix factorization under random initialization.
method New techniques to show saddle-avoidance properties and extend eigenvalue theories.
result Polynomial-time global convergence guarantee for randomly initialized gradient descent on four-layer matrix factorization.
New research finds many coreset methods for logistic regression are not better than simple sampling.
problem Evaluation of coreset methods for reducing data size in logistic regression.
method Comparison of multiple coreset and optimal subsampling methods for logistic regression.
result Many coreset methods do not outperform simple uniform subsampling.
Unified framework for nonconvex matrix completion with linearly parameterized factors.
problem Matrix completion with improved accuracy using linearly parameterized factors.
method Unified nonconvex optimization framework with Correlated Parametric Factorization condition.
result Uniform upper bounds for low-rank estimation at any local minimum.
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
Novel oracle-type inequality for logistic loss in DNNs achieves sharp convergence rates.
problem Generalization analysis for binary classification with DNNs and logistic loss.
method Established an oracle-type inequality to handle the boundedness of the target function.
result Optimal convergence rates for fully connected ReLU DNN classifiers trained with logistic loss.
In this paper, we propose an online algorithm to compute matrix factorizations. Proposed algorithm updates the dictionary matrix and associated coefficients using a single observation at each time. The algorithm performs low-rank updates to dictionary matrix. We derive the algorithm by defining a simple objective funct…
Proposes a robust factor analysis for matrix data.
problem Robust factor analysis for matrix data with heavy-tailed or contaminated data.
method Bilinear factor analysis based on the matrix-variate distribution.
result Significantly higher breakdown point than traditional methods.
New method estimates missingness probabilities for MNAR matrix completion.
problem Bias in matrix completion due to missing not at random data.
method Estimate missingness probabilities using nuclear norm structure.
result Improved matrix completion accuracy without auxiliary information.
Federated multi-view matrix factorization learns from multiple data sources without centralizing user data.
problem Cold-start federated recommendations and multi-view data structure.
method Federated learning framework extended to multi-view matrix factorization.
result Federated multi-view matrix factorization outperforms simpler methods in cold-start federated recommendations.
This study aims to predict vessel stay and delay times at ports to optimize logistics.
problem Uncertainties in maritime logistics, including weather, cargo diversity, and port dynamics, lead to increased costs and inefficiencies.
method Developed predictive analytics to address shortcomings in previous works, using feature analysis and SHAP explanations.
result Predictive analytics can assist in efficient planning and scheduling of port operations, reducing costs and improving logistics.
Matrix factorization is one of the best approaches for collaborative filtering, because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and being very hard to be parallelized, specially with very large matrices. In this paper, we intr…
We want to construct a homological link invariant whose Euler characteristic is MOY polynomial as Khovanov and Rozansky constructed a categorification of HOMFLY polynomial. The present paper gives the first step to construct a categorification of MOY polynomial. For the essential colored planar diagrams with additional…
Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.
problem Understanding what drives healthcare providers to engage with SMS campaigns.
method Used logistic regression, random forest, and neural network models to analyze data.
result Identified key factors influencing engagement with SMS campaigns.
Develops a fast algorithm for fitting multilevel factor models.
problem Fitting multilevel factor models with covariance structure.
method Novel expectation-maximization algorithm tailored for multilevel factor models.
result Shows efficient computation of inverse of positive definite MLR matrix.
Graph neural networks speed up nonnegative matrix factorization.
problem Efficiently factorize nonnegative matrices for various applications.
method Developed a graph neural network that combines bipartite self-attention with ADMM updates.
result Significant acceleration achieved in nonnegative matrix factorization.