Multi-Cancer Samples Clustering via Graph Regularized Low-Rank Representation Method Under Sparse and Symmetric Constraints

BMC Bioinformatics- United Kingdom

doi 10.1186/s12859-019-3231-5

Full Text

Abstract

Available in

Date

Authors

Publisher

Springer Science and Business Media LLC

Related search

Low-Rank Representation With Graph Constraints for Robust Visual Tracking

IEICE Transactions on Information and Systems
Electronic EngineeringPattern RecognitionHardwareComputer VisionElectricalArchitectureArtificial IntelligenceSoftware
2017English

Robust Spectral Clustering via Sparse Representation

2018English

Recovery of Low-Rank Matrices Under Affine Constraints via a Smoothed Rank Function

IEEE Transactions on Signal Processing
Electronic EngineeringSignal ProcessingElectrical
2014English

High Resolution Satellite Image Classification Using Multi-Task Joint Sparse and Low-Rank Representation

2016English

Decomposition of Low Rank Multi-Symmetric Tensor

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2017English

Low-Rank and Eigenface Based Sparse Representation for Face Recognition

PLoS ONE
Multidisciplinary
2014English

Regularized L1-Graph for Data Clustering

2014English

Super-Resolution With Nonlocal Regularized Sparse Representation

2010English

Efficient Rank-One Residue Approximation Method for Graph Regularized Non-Negative Matrix Factorization

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2013English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy