Survey of tensor completion algorithms for big data analytics.
problem Filling missing entries in tensors.
method Overview of recent tensor completion algorithms.
result Advances in tensor completion for big data.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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48 results for “data completion”
Paper proposes a clustering algorithm for nonnegative data.
problem Clustering nonnegative data in disjoint subspaces.
method Simple algorithm to cluster nonnegative data.
result Matrix completion algorithm outperforms standard methods.
Develops a two-stage approach for robust tensor completion of visual data.
problem Estimating missing values in high-order data with outliers.
method Coarse-to-fine framework and M-estimator-based robust tensor ring recovery.
result Superior performance compared to state-of-the-art robust algorithms.
New method for matrix completion under complex missing data patterns.
problem Matrix completion with complex missing data patterns.
method Estimate the probability matrix of observation via low-rank matrix estimation and use inverse probabilities weighting to complete the target matrix.
result Optimal asymptotic convergence rates for observation probabilities and target matrix estimation.
New method improves tensor completion for weakly-dependent spatiotemporal data.
problem Improving tensor completion for weakly-dependent data on graphs.
method Introducing -norm and Graph Laplacian penalties for low-rank tensor decomposition and completion.
result Improved performance in metro passenger flow prediction.
A new training method improves autoregressive data completion efficiency.
problem Efficiently completing missing data in autoregressive models.
method Proposed an alternative training procedure (OA++) that reduces overfitting and leverages prior knowledge.
result OA++ achieves better performance with fewer computations and less overfitting.
Paper proposes a new method for fast matrix completion.
problem Challenges in matrix completion, especially for images with heterogeneous data.
method Sparse reverse of principal component analysis.
result The method efficiently reconstructs matrices with missing data.
With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel meth…
New model for high rank matrix completion with online and batch methods.
problem Matrix completion for high rank matrices with latent structure.
method Kernel trick to map data into a high dimensional feature space, explicit parametrization of low dimensional subspace, online fitting procedure.
result Online method can handle streaming data and adapt to non-stationary latent structure.
Causal Matrix Completionecon.EM
New method corrects bias in missing data for matrix completion.
problem Missing data bias in matrix completion.
method Causal model and synthetic nearest neighbors (SNN) method.
result Synthetic nearest neighbors (SNN) method provides consistent and normal estimates.
Paper determines rank of unknown low-rank data.
problem Determining the rank of unknown low-rank data.
method Analyzes various data models and provides upper bounds on rank based on sampled entries.
result Upper bounds on rank are equal to the true rank in many cases.
Bayesian non-linear matrix completion tackles large, sparse data.
problem Predict missing elements in large, sparsely observed matrices.
method Bayesian Gaussian process latent variable models with data-parallel distributed computation.
result Scalable Bayesian non-linear matrix completion outperforms linear methods.
Tensor completion improves EEG-based BCI performance with missing data.
problem Improving classification accuracy in BCI systems with noisy EEG data.
method Tensor decomposition models to infer missing entries in multidimensional EEG datasets.
result Tensor completion algorithms enhance BCI classification accuracy with missing data.
A very simple interpretation of matrix completion problem is introduced based on statistical models. Combined with the well-known results from missing data analysis, such interpretation indicates that matrix completion is still a valid and principled estimation procedure even without the missing completely at random (M…
The paper proposes methods for predicting missing values in mixed data matrices.
problem Matrix completion for mixed data types (continuous, binary, ordinal).
method Generalized latent factor models for low-rank matrix estimation with entrywise consistency.
result Tight probabilistic error bounds for the proposed estimators.
Applications of a completeness lemma in minimal surface theory to various classes of surfacesmath.DG
We give several applications of a lemma on completeness used by Osserman to show the meromorphicity of Weierstrass data for complete minimal surfaces with finite total curvature. Completeness and weak completeness are defined for several classes of surfaces which admit singular points. The completeness lemma is a usefu…
Paper proposes a novel method to improve matrix completion with median loss for large datasets.
problem Matrix completion with absolute deviation loss for large-scale data.
method Proposes a refinement step using pseudo data to improve inefficient estimators of median matrix completion.
result Turns inefficient estimators into a rate (near-)optimal matrix completion procedure.
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.
Study on Brownian motion on discrete curve spaces, proving stochastic completeness.
problem Analyzing Brownian motion on spaces of discrete curves.
method Introduced and studied Brownian motion on spaces of discrete regular curves with Sobolev-type metrics.
result All geodesically complete spaces of discrete regular curves are stochastically complete.
Framework for completing computational graphs using Gaussian Processes.
problem Completing computational graphs from incomplete data.
method Using Gaussian Processes to approximate unknown functions and recover unobserved variables.
result Efficiently completes computational graphs with fewer data points.
A new framework improves tensor completion accuracy by considering numerical priors.
problem Tensor completion accuracy loss due to ignoring numerical priors.
method Generalized CP Decomposition Tensor Completion (GCDTC) framework incorporating numerical priors.
result GCDTC framework outperforms state-of-the-arts in non-negative tensor completion.
Proposes CVKT to complete missing kernel matrices across multiple views.
problem Missing data in multiple views of kernel matrices.
method Cross-View Kernel Transfer (CVKT) with kernel alignment.
result Predicts missing values in kernel matrices using other views' data.
New deep learning model for matrix completion combining linear and nonlinear relationships.
problem Matrix completion considering only linear or nonlinear relations, ignoring latent relationships.
method Combines linear and nonlinear models in a latent variables framework, using a deep neural network with two branches for columns and rows, and manifold learning as an auxiliary task.
result Experimental results show the proposed method outperforms state-of-the-art matrix completion methods.
A new tensor completion method handles missing data with missing not at random entries.
problem Handling missing data in tensors where the probability of observation depends on other entries.
method Estimate propensities using convex relaxation, then use higher-order SVD with inverse propensities weights.
result Finite-sample error bounds on the completed tensor are provided.
Paper proposes a method to improve autoencoder-based matrix completion models.
problem Overfitting in autoencoder-based models with limited training data.
method Data-dependent regularization using manifold learning as an auxiliary task.
result The proposed model achieves high reconstruction accuracy in well-known datasets.
The study challenges the notion that partial data annotation is inferior, suggesting it can sometimes outperform complete annotation.
problem The inefficiency and high cost of completely annotating structured data.
method Information theoretic formulation applied to three diverse structured learning tasks.
result Learning from partial structures can sometimes outperform learning from complete ones.
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels. These aspects are necessary in practical app…
Proves subspace tracking with missing data and improves matrix completion.
problem Subspace tracking in the presence of missing data.
method Modified robust subspace tracking algorithm.
result Proves subspace estimates are close to true subspaces under mild assumptions.
Collective Matrix Completionstat.ML
This work tackles collective matrix completion with multiple and heterogeneous data sources.
problem Reconstructing data from multiple heterogeneous matrices.
method Estimation based on minimizing goodness-of-fit and nuclear norm penalization of the whole collective matrix.
result Proposed estimators achieve fast rates of convergence under two settings.
Paper tackles matrix completion with a mixture of low-rank matrices.
problem Matrix completion with a mixture of low-rank matrices.
method Generalized matrix completion model (MMC) with theoretical and practical contributions.
result MMC provides a more accurate model for recommender systems and clustering.
Selective sampling improves matrix completion with known structure.
problem Reconstructing a low-rank matrix with incomplete data.
method Designing observation sets based on matrix structure and selective sampling.
result Improved reconstruction accuracy with selective sampling.
New method for robust matrix completion with mixed data types.
problem Recovering a structured low rank matrix with mixed data types.
method Proposes a computationally feasible statistical approach with strong recovery guarantees for mixed data types.
result Strong recovery guarantees for low rank matrix completion with mixed data types.
New method for tensor completion using nonconvex dual total variation.
problem Tensor completion from partial measurements with exponential-family noise.
method Proposed dual-TV (DTV) regularizers for tensor completion under exponential-family noise.
result Theoretical upper bounds on recovery error for tensor completion.
RTC-GTNLN model recovers traffic data from missing values and noise.
problem Simultaneous missing data and noise in traffic data.
method Gradient tensor nuclear L1-L2 norm for robust tensor completion.
result RTC-GTNLN model outperforms existing methods in complex recovery scenarios.
Study compares LRMC algorithms under dependent sampling in various applications.
problem Recovering missing entries in partially observed low-rank matrices with dependent sampling.
method Various LRMC algorithms tested under dependent sampling in different contexts.
result Performance differences among LRMC algorithms under dependent sampling.
Paper uses tensor completion to estimate HVAC fan power baselines.
problem Estimating HVAC fan power without demand response.
method Tensor completion for multi-dimensional data analysis.
result Tensor completion outperforms existing baselining methods.
RMCSE improves voltage estimation in low-observability distribution systems.
problem Insufficient measurements in distribution system state estimation.
method Combines matrix completion and power system model, minimizes rank and residual with different weights.
result Robust voltage estimation in low-observability systems without bad data detection.
Novel LRMC tackles missing data and outliers in large-scale low-rank data recovery.
problem Missing data and extreme outliers in low-rank data analysis.
method Learned Robust Matrix Completion (LRMC) using deep unfolding and flexible neural network framework.
result LRMC achieves optimum performance with low computational complexity and linear convergence.
Unified model for tensor completion using low-rank and sparse Tucker decomposition.
problem Estimating missing data from incomplete tensor measurements.
method Unified low-rank and sparse enhanced Tucker decomposition model with ADMM.
result Our model achieves higher recovery accuracy on various real-world data sets.
Paper proves tensor ring completion with high probability using convex optimization.
problem Recovering a multi-dimensional array from limited measurements.
method Tensor ring decomposition and convex optimization.
result High probability exact recovery with n^{d/2} r^2 ln^7(n^{d/2}) samples.
Paper explores robustness of CCS model for matrix completion.
problem Robustness of cross-concentrated sampling model against sparse outliers.
method Proposes Robust CUR Completion (RCURC) algorithm for efficient non-convex iterative matrix completion.
result Empirical validation of RCURC's efficiency and robustness in synthetic and real datasets.
A novel regularizer of the PARAFAC decomposition factors capturing the tensor's rank is proposed in this paper, as the key enabler for completion of three-way data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and predictio…
Paper proposes a method to recover rankings from limited comparisons using low-rank matrix completion.
problem Rank aggregation from pairwise comparisons with limited and noisy data.
method Low-rank matrix completion, alternating minimization algorithm, maximum likelihood estimation.
result Improved algorithm performance over state-of-the-art methods.
The paper recovers missing data entries of high-rank matrices using polynomial polynomials.
problem Recovering missing entries of high-rank matrices with low intrinsic dimension.
method Developed a new polynomial matrix completion method using the kernel trick and relaxation of rank objective.
result Identified complete matrix of minimum intrinsic dimension by minimizing rank in high-dimensional feature space.
Proposes a transductive matrix completion method with calibration for multi-task learning.
problem Improving multi-task learning with multiple related data sources.
method Transductive matrix completion with calibration constraint.
result The proposed algorithm recovers incomplete feature and target matrices with improved results.
Paper extends matrix completion to nonlinear algebraic varieties.
problem Matrix completion for nonlinear algebraic varieties.
method Tensorization and Kronecker product approach.
result New method outperforms existing state-of-the-art methods.
Bayesian model improves image completion accuracy by automatically learning low rank structure.
problem Improving image completion accuracy with limited data and avoiding overfitting.
method Developed a Bayesian low rank tensor ring model with multiplicative interaction and Student-T distribution for sparse core factors.
result The proposed method outperforms state-of-the-art image completion techniques, especially in recovery accuracy.
Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion in extremely large-scale applications, such as seismic data interpolation. In this paper, we consider matrix completion formulations designed to hit a targ…