Publications related to Tensor rank decomposition

Unlabeled Principal Component Analysis and Matrix Completion

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Microtome Publishing2024

STREAMING TENSOR TRAIN APPROXIMATION

Daniel Kressner

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...

Siam Publications2023

Quantum mixed state compiling

Zoë Holmes

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generaliz ...

IOP Publishing Ltd2023

Functional Tucker Approximation Using Chebyshev Interpolation

Daniel Kressner, Christoph Max Strössner

This work is concerned with approximating a trivariate function defined on a tensor-product domain via function evaluations. Combining tensorized Chebyshev interpolation with a Tucker decomposition of low multilinear rank yields function approximations tha ...

SIAM PUBLICATIONS2021

Emergence of representative signals for sudden stratospheric warmings beyond current predictable lead times

Guillaume Romain Obozinski, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville

Major sudden stratospheric warmings (SSWs) are extreme wintertime circulation events of the Arctic stratosphere that are accompanied by a breakdown of the polar vortex and are considered an important source of predictability of tropospheric weather on subs ...

Copernicus Gesellschaft Mbh2021

Dual Dynamically Orthogonal approximation of incompressible Navier Stokes equations with random boundary conditions

Fabio Nobile, Eleonora Musharbash

In this paper we propose a method for the strong imposition of random Dirichlet boundary conditions in the Dynamical Low Rank (DLR) approximation of parabolic PDEs and, in particular, incompressible Navier Stokes equations. We show that the DLR variational ...

Elsevier2018

Structure-Preserving Low Multilinear Rank Approximation Of Antisymmetric Tensors

Daniel Kressner

This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, multivariate arrays for which the entries change sign when permuting pairs of indices. We show which ranks can be attained by an antisymmetric tensor and di ...

Siam Publications2017

MRI artifact correction using sparse plus low-rank decomposition of annihilating filter-based hankel matrix

Kyong Hwan Jin

PurposeMagnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as ir ...

Wiley2017

Scalable Low-rank Matrix and Tensor Decomposition on Graphs

Nauman Shahid

In many signal processing, machine learning and computer vision applications, one often has to deal with high dimensional and big datasets such as images, videos, web content, etc. The data can come in various forms, such as univariate or multivariate time ...

EPFL2017

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

Daniel Kressner, André Uschmajew

Low-rank tensor approximation techniques attempt to mitigate the overwhelming complexity of linear algebra tasks arising from high-dimensional applications. In this work, we study the low-rank approximability of solutions to linear systems and eigenvalue p ...

Elsevier Science Inc2016

Multilinear Low-Rank Tensors on Graphs & Applications

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a new framework for the analysis of low- rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new graph based low-rank decomposition which approximates the classical low-rank S ...

2016

Low-rank tensor approximation for high-order correlation functions of Gaussian random fields

Fabio Nobile, Daniel Kressner, Christine Tobler, Rajesh Kumar

Gaussian random fields are widely used as building blocks for modeling stochastic processes. This paper is concerned with the efficient representation of d-point correlations for such fields, which in turn enables the representation of more general stochas ...

2015

Reverberant Audio Source Separation via Sparse and Low-Rank Modeling

Pierre Vandergheynst

The performance of audio source separation from underde- termined convolutive mixture assuming known mixing filters can be significantly improved by using an analysis sparse prior optimized by a reweighting l1 scheme and a wideband data- fidelity term, as ...

IEEE Institute of Electrical and Electronics Engineers2014

Reconstruction of Large, Irregularly Sampled Multidimensional Images. A Tensor-Based Approach

Michaël Unser

Many practical applications require the reconstruction of images from irregularly sampled data. The spline formalism offers an attractive framework for solving this problem; the currently available methods, however, are hard to deploy for large-scale inter ...

Institute of Electrical and Electronics Engineers2011

Dynamic Texture Analysis and Synthesis using Tensor Decomposition

Sabine Süsstrunk, Luciano Sbaiz, Roberto Costantini

Dynamic textures are sequences of images showing temporal regularity, such as smoke, flames, flowing water, or moving grass. Despite being a multidimensional signal, existing models reshape the dynamic texture into a 2D signal for analysis. In this article ...

2006