arXiv research

JULIA combines multi-linear and nonlinear models for tensor completion.

A new framework improves tensor completion accuracy by considering numerical priors.

Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer…

In this paper, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by usin…

Improves group fairness in tensor completion by augmenting tensors with balanced entities.

A new tensor completion method handles missing data with missing not at random entries.

Develops a two-stage approach for robust tensor completion of visual data.

We prove several Liouville-type non-existence theorems for higher order Codazzi tensors and classical Codazzi tensors on complete and compact Riemannian manifolds, in particular. These results will be obtained by using theorems of the connections between the geometry of a complete smooth manifold and the global behavio…

New algorithm for nonnegative tensor completion with linear convergence rate.

Paper proposes a new model for noisy tensor completion.

New method for tensor completion using nonconvex dual total variation.

We study tensor completion in the agnostic setting. In the classical tensor completion problem, we receive $n$ entries of an unknown rank-$r$ tensor and wish to exactly complete the remaining entries. In agnostic tensor completion, we make no assumption on the rank of the unknown tensor, but attempt to predict unknown …

Paper improves tensor completion by reducing sample entries needed.

Study uncovers statistical optimality of nonconvex tensor completion methods.

In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Proce…

New tensor completion method reduces impact of outliers.

Proposes a method to recover sparse tensors with covariate info.

SG-NTF completes HDI tensors with spectral mapping and spatio-temporal gating.

Introduces t-CCS for flexible tensor sampling.

New method estimates and completes tensors from ordinal data, improving accuracy and efficiency.

The paper tackles tensor factorization and completion from noisy data.

Unified model for tensor completion using low-rank and sparse Tucker decomposition.

New tensor completion method converges linearly and is highly practical.

New method for tensor completion from specific mode observations.

Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors i…

New algorithms solve tensor problems with random components using SDP.

We obtain the first polynomial-time algorithm for exact tensor completion that improves over the bound implied by reduction to matrix completion. The algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed entries of the tensor…

RTC-GTNLN model recovers traffic data from missing values and noise.

A new tensor completion method using tensor networks with Tucker wrapper.

Completes the proof of curvature tensor existence for Jacobi operators.

Paper uses tensor completion to estimate HVAC fan power baselines.

Gradient descent promotes low-rank solutions in tensor completion.

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular applica…

The paper improves tensor completion bounds using spectral gap.

We analyze low rank tensor completion (TC) using noisy measurements of a subset of the tensor. Assuming a rank-$r$, order-$d$, $N \times N \times \cdots \times N$ tensor where $r=O(1)$, the best sampling complexity that was achieved is $O(N^{\frac{d}{2}})$, which is obtained by solving a tensor nuclear-norm minimizatio…

We consider the problem of low canonical polyadic (CP) rank tensor completion. A completion is a tensor whose entries agree with the observed entries and its rank matches the given CP rank. We analyze the manifold structure corresponding to the tensors with the given rank and define a set of polynomials based on the sa…

One of the current issues in Brain-Computer Interface is how to deal with noisy Electroencephalography measurements organized as multidimensional datasets. On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to captur…

New method improves tensor completion by selectively preserving important elements.

Low-rank tensor completion recovers missing entries based on different tensor decompositions. Due to its outstanding performance in exploiting some higher-order data structure, low rank tensor ring has been applied in tensor completion. To further deal with its sensitivity to sparse component as it does in tensor princ…

We propose a set of convex low rank inducing norms for a coupled matrices and tensors (hereafter coupled tensors), which shares information between matrices and tensors through common modes. More specifically, we propose a mixture of the overlapped trace norm and the latent norms with the matrix trace norm, and then, w…

Optimizes tensor completion using geodesics on Segre manifolds.

New method estimates tensors from noisy data with missing entries.

In this paper, we study vacuum static spaces with the complete divergence of the Bach tensor and Weyl tensor. First, we prove that the vanishing of complete divergence of the Bach tensor and Weyl tensor implies the harmonicity of the metric, and we present examples in which these conditions do not imply Bach flatness. …

The goal of tensor completion is to fill in missing entries of a partially known tensor (possibly including some noise) under a low-rank constraint. This may be formulated as a least-squares problem. The set of tensors of a given multilinear rank is known to admit a Riemannian manifold structure, thus methods of Rieman…

New model analyzes customer churn with tensor completion and binary data.

In this paper, we investigate the sample size requirement for a general class of nuclear norm minimization methods for higher order tensor completion. We introduce a class of tensor norms by allowing for different levels of coherence, which allows us to leverage the incoherence of a tensor. In particular, we show that …

Enhances knowledge graph completion with mixed geometry tensor factorization.

New algorithm improves tensor completion performance.