Results 141 to 150 of about 2,392 (180)

Multi-view Spectral Clustering via Tensor-SVD Decomposition

2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017
Multi-view clustering has attracted considerable attention in recent years, some related approaches always use matrices to represent views, and model by capturing two dimensional structure among views. The critical deficiency of these work is ignoring the space structure information of all views, which results in the mediocre performance of clustering.
Yan Zhang, Weiwei Yang, Bangtian Liu, Geyang Ke, Yan Pan, Jian Yin   +5 more
openaire   +1 more source

Low Rank Tensor STAP filter based on multilinear SVD

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012
Space Time Adaptive Processing (STAP) is a two-dimensional adaptive filtering technique which uses jointly temporal and spatial dimensions to suppress disturbance and to improve target detection. Disturbance contains both the clutter arriving from signal backscattering of the ground and the thermal noise resulting from the sensors noise.
Boizard, Mélanie, Ginolhac, Guillaume, Pascal, Frédéric, Forster, Philippe   +3 more
openaire   +2 more sources

Sequential unfolding SVD for low rank orthogonal tensor approximation

2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008
This paper contributes to the field of N-way (N ges 3) tensor decompositions, which are increasingly popular in various signal processing applications. A novel PARATREE decomposition structure is introduced, accompanied with sequential unfolding SVD (SUSVD) algorithm.
Jussi Salmi, Andreas Richter, Visa Koivunen   +2 more
openaire   +1 more source

SVD-based algorithms for tensor wheel decomposition

Advances in Computational Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mengyu Wang, Honghua Cui, Hanyu Li
openaire   +2 more sources

Online Tensor Completion and Free Submodule Tracking With The T-SVD

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
We propose a new online algorithm, called TOUCAN, for the tensor completion problem of imputing missing entries of a low tubal-rank tensor using the tensor-tensor product (t- product) and tensor singular value decomposition (t-SVD) algebraic framework.
Kyle Gilman, Laura Balzano
openaire   +1 more source

Iteratively reweighted tensor SVD for robust multi-dimensional harmonic retrieval

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
In this paper, parameter estimation for multi-dimensional sinusoids in additive impulsive noise is addressed. Our underlying idea is to minimize the lp-norm of the residual error tensor, where 1 < p < 2, and transform this problem to an iterative l2-norm minimization.
Weize Sun, Xin Lin, H. C. So, Lei Huang, Qiang Li   +4 more
openaire   +1 more source

SVD-based algorithms for fully-connected tensor network decomposition

Computational and Applied Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mengyu Wang, Hanyu Li
openaire   +1 more source

Truncated Hierarchical SVD for image sequences, represented as third order tensor

2017 8th International Conference on Information Technology (ICIT), 2017
In this work is presented new algorithm, called Truncated Hierarchical SVD (THSVD), aimed at the processing of sequences of correlated images, represented as third-order tensors. The algorithm is based on the multiple calculation of the matrix SVD for elementary tensors (ET) of size 2×2×2, which build the tensor of size N×N×N, when N=2n.
Roumen Kountchev, Roumiana Kountcheva
openaire   +1 more source

A Two-Step Image Inpainting Algorithm Using Tensor SVD

2015
In this paper, we present a novel exemplar-based image inpainting algorithm using the higher order singular value decomposition (HOSVD). The proposed method performs inpainting of the target image in two steps. At the first step, the target region is inpainted using HOSVD-based filtering of the candidate patches selected from the source region.
Mrinmoy Ghorai, Sekhar Mandal, Bhabatosh Chanda   +2 more
openaire   +1 more source

Tensor learningusing N-mode SVD for dynamic background modelling and subtraction

2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC), 2017
Background modelling and subtraction is an essential component in motion analysis with wide range of applications in computer vision, whereas the task becomes more challenging in context of complex scenarios such as dynamic backgrounds. In this paper, we address the problem of modelling dynamic backgrounds in online tensor leaning framework.
Sheheryar Khan, Guoxia Xu, Hong Yan
openaire   +1 more source