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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.

168,742 papers · 148 categories

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12 results for “t-SVD”

Handling The Non-Smooth Challenge in Tensor SVD: A Multi-Objective Tensor Recovery Frameworkstat.ML

New method tackles non-smooth tensor data for better recovery.

problem Non-smooth changes in tensor data degrade traditional t-SVD methods.

method Learnable tensor nuclear norm, Alternating Proximal Multiplier Method (APMM), multi-objective tensor recovery framework.

result The proposed method effectively recovers tensor data with non-smooth changes.

tensor recovery non-smooth tensor data t-SVD multi-objective Alternating Proximal Multiplier Method

2023-11-23abs ↗pdf ↗

Exact tensor completion using t-SVDcs.LG

In this paper we focus on the problem of completion of multidimensional arrays (also referred to as tensors) from limited sampling. Our approach is based on a recently proposed tensor-Singular Value Decomposition (t-SVD) [1]. Using this factorization one can derive notion of tensor rank, referred to as the tensor tubal…

2015-02-16abs ↗pdf ↗

Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descentcs.LG

Efficiently recovers low-tubal-rank tensors from few measurements.

problem Recovering tensors with low tubal-rank from limited measurements.

method Factorization and factorized gradient descent.

result Factorized gradient descent reduces computational costs and storage requirements.

tensor recovery low-tubal-rank factorization gradient descent t-SVD

2024-01-22abs ↗pdf ↗

T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysiscs.LG

Tensor completion and robust principal component analysis have been widely used in machine learning while the key problem relies on the minimization of a tensor rank that is very challenging. A common way to tackle this difficulty is to approximate the tensor rank with the $\ell_1-$ norm of singular values based on its …

2019-04-23abs ↗pdf ↗

Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representationstat.ML

Paper develops a method to robustly cluster tensors with outliers.

problem Clustering tensors contaminated by outliers or sample-specific corruptions.

method Transformed Tensor Low-Rank Representation (OR-TLRR) method.

result Provably recovers row space of clean data and detects outliers.

Tensor Clustering Outliers Robustness Low-Rank Representation t-SVD

2023-07-18abs ↗pdf ↗

Effective Streaming Low-tubal-rank Tensor Approximation via Frequent Directionscs.LG

Paper improves tensor approximation for streaming data.

problem Challenges in finding accurate low-tubal-rank tensor approximations in streaming settings.

method Extends Frequent Directions for efficient low-tubal-rank tensor approximation.

result The new algorithm achieves arbitrarily small approximation error with linear sketch size growth.

tensor approximation streaming data Frequent Directions t-SVD low-tubal-rank

2021-08-23abs ↗pdf ↗

Robust Tensor Principal Component Analysis: Exact Recovery via Deterministic Modelstat.ML

Paper proposes a new method for exact recovery in robust tensor principal component analysis.

problem Exact recovery of low-rank and sparse components in tensors.

method Proposes a new method based on tensor-tensor product and t-SVD to solve a convex optimization problem.

result Exact recovery achieved in a deterministic fashion without randomness assumptions.

tensor principal component analysis robust tensor PCA tensor-tensor product t-SVD convex optimization

2020-08-05abs ↗pdf ↗

Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descentstat.ML

ScaledGD algorithm estimates low-rank tensors efficiently from corrupted data.

problem Estimating meaningful information from corrupted tensor data.

method Scaled gradient descent (ScaledGD) algorithm with tailored spectral initializations.

result ScaledGD achieves linear convergence at a constant rate independent of condition number.

Tensor estimation Low-rank tensor Corrupted data Gradient descent Spectral initialization

2025-01-03abs ↗pdf ↗

IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrayseess.SP

A neural network, IHT-Net, improves DOA estimation with sparse arrays.

problem Single-snapshot DOA estimation with sparse arrays in dynamic settings.

method IHT-inspired neural network with recurrent neural network and autoencoders.

result IHT-Net achieves faster convergence and higher accuracy in DOA estimation.

DOA estimation sparse arrays neural networks directional signal processing recurrent neural networks

2023-09-15abs ↗pdf ↗

Tensor p-shrinkage nuclear norm for low-rank tensor completioncs.LG

In this paper, a new definition of tensor p-shrinkage nuclear norm (p-TNN) is proposed based on tensor singular value decomposition (t-SVD). In particular, it can be proved that p-TNN is a better approximation of the tensor average rank than the tensor nuclear norm when p < 1. Therefore, by employing the p-shrinkage nu…

2019-07-09abs ↗pdf ↗

Exact Tensor Completion from Sparsely Corrupted Observations via Convex Optimizationcs.LG

This paper conducts a rigorous analysis for provable estimation of multidimensional arrays, in particular third-order tensors, from a random subset of its corrupted entries. Our study rests heavily on a recently proposed tensor algebraic framework in which we can obtain tensor singular value decomposition (t-SVD) that …

2017-08-02abs ↗pdf ↗

Tensor Q-Rank: New Data Dependent Definition of Tensor Rankcs.LG

Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of da…

2019-10-26abs ↗pdf ↗