NucNormZSL: nuclear norm-based domain adaptation in zero-shot learning
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06461-1
Abstract: The ability of human beings to recognize novel concepts has attracted significant attention in the research community. Zero-shot learning, also known as zero-data learning, seeks to build models that can recognize novel class instances even… read more here.
Keywords: nuclear norm; shot learning; class; zero shot ... See more keywords
Enhancing Prony’s method by nuclear norm penalization and extension to missing data
Sign Up to like & getrecommendations! Published in 2017 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-017-1062-2
Abstract: Prony’s method is a widely used method for modelling signals using a finite sum of exponential terms. It has innumerable applications in weather modelling, finance, medical signal analysis, image compression, time series analysis, power grids,… read more here.
Keywords: nuclear norm; prony method; method; method nuclear ... See more keywords
Iterative weighted nuclear norm for X-ray cardiovascular angiogram image denoising
Sign Up to like & getrecommendations! Published in 2017 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-017-1105-8
Abstract: Low-rank regularization approximated by a nuclear norm has been proven its ability in image denoising. However, the nuclear norm is just a suboptimization of the rank norm, resulting in a big error when reducing noise.… read more here.
Keywords: nuclear norm; image; iterative weighted; image denoising ... See more keywords
Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Sound and Vibration"
DOI: 10.1016/j.jsv.2017.03.044
Abstract: Abstract It is a fundamental task in the machine fault diagnosis community to detect impulsive signatures generated by the localized faults of bearings. The main goal of this paper is to exploit the low-rank physical… read more here.
Keywords: fault; nuclear norm; fault detection; bearing fault ... See more keywords
Non-convex weighted ℓp nuclear norm based ADMM framework for image restoration
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.05.073
Abstract: Abstract Inspired by the fact that the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies.… read more here.
Keywords: image restoration; nuclear norm; image; non convex ... See more keywords
Matrix completion with capped nuclear norm via majorized proximal minimization
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.07.066
Abstract: Abstract We investigate the problem of matrix completion with capped nuclear norm regularization. Different from most existing regularizations that minimize all the singular values simultaneously, capped nuclear norm only penalties the singular values smaller than… read more here.
Keywords: nuclear norm; proximal minimization; minimization; matrix completion ... See more keywords
Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2940664
Abstract: In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor… read more here.
Keywords: tensor; tensor nuclear; norm minimization; rank tensor ... See more keywords
An Optimal Hybrid Nuclear Norm Regularization for Matrix Sensing With Subspace Prior Information
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3009688
Abstract: Matrix sensing refers to recovering a low-rank matrix from a few linear combinations of its entries. This problem naturally arises in many applications including recommendation systems, collaborative filtering, seismic data interpolation and wireless sensor networks.… read more here.
Keywords: information; matrix sensing; nuclear norm; hybrid nuclear ... See more keywords
Closed-Loop Subspace Identification for Stable/ Unstable Systems Using Data Compression and Nuclear Norm Minimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3154017
Abstract: This paper provides a subspace method for closed-loop identification, which clearly specifies the model order from noisy measurement data. The method can handle long I/O data of the target system to be noise-tolerant and determine… read more here.
Keywords: norm minimization; closed loop; nuclear norm; data compression ... See more keywords
A Tensor Method Based on Enhanced Tensor Nuclear Norm and Hypergraph Laplacian Regularization for Pan-Cancer Omics Data Analysis
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3231908
Abstract: As a powerful data representation technique, tensor robust principal component analysis (TRPCA) has been widely used for clustering and feature selection tasks. However, it ignores the significant difference in singular values of tensor data and… read more here.
Keywords: tensor; enhanced tensor; nuclear norm; tensor nuclear ... See more keywords
Nonconvex Log-Sum Function-Based Majorization–Minimization Framework for Seismic Data Reconstruction
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2909776
Abstract: Because of the fact that complete seismic data can have a low rank in the frequency-space (f-x) domain, rank-reduction methods are classical techniques used for seismic data reconstruction. Models that employ nuclear-norm minimization signify convex… read more here.
Keywords: data reconstruction; seismic data; minimization; function ... See more keywords