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Gui-Fu Lu
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2020 – today
- 2025
- [j63]Yangfan Du
, Gui-Fu Lu:
Joint local smoothness and low-rank tensor representation for robust multi-view clustering. Pattern Recognit. 157: 110944 (2025) - 2024
- [j62]Yaozu Kan
Multi-view clustering using a flexible and optimal multi-graph fusion method. Eng. Appl. Artif. Intell. 128: 107452 (2024) - [j61]Tong Wu
Coupled double self-expressive subspace clustering with low-rank tensor learning. Expert Syst. Appl. 255: 124619 (2024) - [j60]Xiaoxing Guo
Tensor-based global block-diagonal structure radiation for incomplete multiview clustering. Expert Syst. Appl. 255: 124788 (2024) - [j59]Bing Cai
Aligned multi-view clustering for unmapped data via weighted tensor nuclear norm and adaptive graph learning. Neurocomputing 597: 128016 (2024) - [j58]Bing Cai
Complete multi-view subspace clustering via auto-weighted combination of visible and latent views. Inf. Sci. 665: 120381 (2024) - [j57]Xiaoxing Guo
Comprehensive consensus representation learning for incomplete multiview subspace clustering. Inf. Sci. 678: 120935 (2024) - [j56]Tong Wu, Gui-Fu Lu:
Coupled double consensus multi-graph fusion for multi-view clustering. Inf. Sci. 680: 121186 (2024) - [j55]Yaozu Kan
Tensor low-rank representation combined with consistency and diversity exploration. Int. J. Mach. Learn. Cybern. 15(11): 5173-5184 (2024) - [j54]Lili Fan, Gui-Fu Lu, Ganyi Tang, Yong Wang:
A fast anchor-based graph-regularized low-rank representation approach for large-scale subspace clustering. Mach. Vis. Appl. 35(1): 15 (2024) - [j53]Yaozu Kan
An adaptive kernel dictionary-based low-rank representation method for subspace clustering. Neural Networks 178: 106434 (2024) - [j52]Bing Cai
Auto-weighted multi-view clustering with the use of an augmented view. Signal Process. 215: 109286 (2024) - [j51]Yangfan Du
Robust Least Squares Regression for Subspace Clustering: A Multi-View Clustering Perspective. IEEE Trans. Image Process. 33: 216-227 (2024) - [j50]Bing Cai
Tensorized Scaled Simplex Representation for Multi-View Clustering. IEEE Trans. Multim. 26: 6621-6631 (2024) - 2023
- [j49]Shouhang Wang, Yong Wang
Mixed structure low-rank representation for multi-view subspace clustering. Appl. Intell. 53(15): 18470-18487 (2023) - [j48]Guangyan Ji
Scalable incomplete multi-view clustering via tensor Schatten p-norm and tensorized bipartite graph. Eng. Appl. Artif. Intell. 123(Part B): 106379 (2023) - [j47]Guangyan Ji
Unbalanced incomplete multi-view clustering based on low-rank tensor graph learning. Expert Syst. Appl. 225: 120055 (2023) - [j46]Liang Yao
Multi-view clustering based on a multimetric matrix fusion method. Expert Syst. Appl. 228: 120272 (2023) - [j45]Yangfan Du
Robust and optimal neighborhood graph learning for multi-view clustering. Inf. Sci. 631: 429-448 (2023) - [j44]Yangfan Du
Robust subspace clustering via multi-affinity matrices fusion. Knowl. Based Syst. 278: 110874 (2023) - [j43]Guangyan Ji
Consensus latent incomplete multi-view clustering with low-rank tensor constraint. Int. J. Mach. Learn. Cybern. 14(11): 3813-3825 (2023) - [j42]Rong Tang
Multi-view subspace similarity learning based on t-SVD. Multim. Tools Appl. 82(29): 45605-45620 (2023) - [j41]Rong Tang
Consensus similarity learning based on tensor nuclear norm. Mach. Vis. Appl. 34(1): 5 (2023) - [j40]Guangyan Ji, Gui-Fu Lu:
A late fusion scheme for multi-graph regularized NMF. Mach. Vis. Appl. 34(5): 96 (2023) - [j39]Liang Yao
Multi-view clustering indicator learning with scaled similarity. Pattern Anal. Appl. 26(3): 1395-1406 (2023) - [j38]Bing Cai
High-order manifold regularized multi-view subspace clustering with robust affinity matrices and weighted TNN. Pattern Recognit. 134: 109067 (2023) - [j37]Jinbiao Zhao, Gui-Fu Lu:
Clean affinity matrix learning with rank equality constraint for multi-view subspace clustering. Pattern Recognit. 134: 109118 (2023) - [c5]Yaozu Kan, Gui-Fu Lu, Yangfan Du, Guangyan Ji:
Efficient Tensor Low-Rank Representation with a Closed Form Solution. ACPR (2) 2023: 326-339 - 2022
- [j36]Gui-Fu Lu
Robust low-rank representation with adaptive graph regularization from clean data. Appl. Intell. 52(5): 5830-5840 (2022) - [j35]Gui-Fu Lu
Latent multi-view self-representations for clustering via the tensor nuclear norm. Appl. Intell. 52(6): 6539-6551 (2022) - [j34]Jinbiao Zhao, Gui-Fu Lu:
Clean and robust affinity matrix learning for multi-view clustering. Appl. Intell. 52(14): 15899-15915 (2022) - [j33]Bing Cai
Tensor subspace clustering using consensus tensor low-rank representation. Inf. Sci. 609: 46-59 (2022) - [j32]Guangyan Ji
One-step incomplete multiview clustering with low-rank tensor graph learning. Inf. Sci. 615: 209-225 (2022) - [j31]Liang Yao
Double structure scaled simplex representation for multi-view subspace clustering. Neural Networks 151: 168-177 (2022) - [c4]Lili Fan, Gui-Fu Lu, Ganyi Tang, Yong Wang:
Kernel Subspace Clustering based on Block Diagonal Representation and Sparse Constraints. DSA 2022: 360-367 - [c3]Rong Tang, Gui-Fu Lu:
Constrained Multilinear Multi-view Subspace Representation Learning for Clustering based on Tensor Nuclear Norm. DSA 2022: 685-691 - 2021
- [j30]Gui-Fu Lu
Multi-view subspace clustering with Kronecker-basis-representation-based tensor sparsity measure. Mach. Vis. Appl. 32(6): 123 (2021) - [j29]Shuo Chen
δ-Norm-Based Robust Regression With Applications to Image Analysis. IEEE Trans. Cybern. 51(6): 3371-3383 (2021) - [c2]Caifeng Yang, Tao Liu, Gui-Fu Lu, Zhenxin Wang, Zhi Deng:
Improved Non-negative Matrix Factorization Algorithm for Sparse Graph Regularization. ICPCSEE (1) 2021: 221-232 - [c1]Lili Fan, Gui-Fu Lu, Yong Wang, Tao Liu:
Block Diagonal Sparse Subspace Clustering. WCSP 2021: 1-6 - 2020
- [j28]Yong Wang
Using Algebra Graph Representation to Detect Pairwise-Constraint Software Faults. IEEE Access 8: 184550-184559 (2020) - [j27]Gui-Fu Lu
Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint. Neural Networks 125: 214-223 (2020)
2010 – 2019
- 2019
- [j26]Wei-Xin Yao, Dan Yang, Gui-Fu Lu, Jun Wang:
A Fast Rough Mode Decision Algorithm for HEVC. J. Inf. Process. Syst. 15(3): 492-499 (2019) - 2018
- [j25]Gui-Fu Lu, Jian Zou, Yong Wang, Zhongqun Wang:
Sparse L1-norm-based linear discriminant analysis. Multim. Tools Appl. 77(13): 16155-16175 (2018) - [j24]Gui-Fu Lu, Yong Wang
Matrix exponential based discriminant locality preserving projections for feature extraction. Neural Networks 97: 127-136 (2018) - 2017
- [j23]Gui-Fu Lu, Jian Zou, Yong Wang
L1-norm based null space discriminant analysis. Multim. Tools Appl. 76(14): 15801-15816 (2017) - [j22]Lei Luo, Jian Yang
Robust Image Regression Based on the Extended Matrix Variate Power Exponential Distribution of Dependent Noise. IEEE Trans. Neural Networks Learn. Syst. 28(9): 2168-2182 (2017) - 2016
- [j21]Gui-Fu Lu, Ganyi Tang, Jian Zou:
Spare L1-norm-based maximum margin criterion. J. Vis. Commun. Image Represent. 38: 11-17 (2016) - [j20]Jian Zou, Gui-Fu Lu, Yue Zhang, Chuancai Liu:
Generalizing intersection kernel support vector machines for color texture based recognition. J. Vis. Commun. Image Represent. 41: 1-14 (2016) - [j19]Gui-Fu Lu, Jian Zou, Yong Wang
A New and Fast Implementation of Orthogonal LDA Algorithm and Its Incremental Extension. Neural Process. Lett. 43(3): 687-707 (2016) - [j18]Gui-Fu Lu, Yong Wang
Graph Maximum Margin Criterion for Face Recognition. Neural Process. Lett. 44(2): 387-405 (2016) - [j17]Gui-Fu Lu, Jian Zou, Yong Wang
L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso. Pattern Recognit. 55: 207-214 (2016) - [j16]Gui-Fu Lu, Jian Zou, Yong Wang
L1-norm-based principal component analysis with adaptive regularization. Pattern Recognit. 60: 901-907 (2016) - [j15]Gui-Fu Lu, Yong Wang
Low-Rank Matrix Factorization With Adaptive Graph Regularizer. IEEE Trans. Image Process. 25(5): 2196-2205 (2016) - 2015
- [j14]Gui-Fu Lu, Jian Zou, Yong Wang
Incremental learning from chunk data for IDR/QR. Image Vis. Comput. 36: 1-8 (2015) - [j13]Gui-Fu Lu, Jian Zou:
Incremental maximum margin criterion based on eigenvalue decomposition updating algorithm. Mach. Vis. Appl. 26(6): 807-817 (2015) - 2013
- [j12]Gui-Fu Lu, Yong Wang
Improved complete neighbourhood preserving embedding for face recognition. IET Comput. Vis. 7(1): 71-79 (2013) - [j11]Gui-Fu Lu, Wenming Zheng:
Complexity-reduced implementations of complete and null-space-based linear discriminant analysis. Neural Networks 46: 165-171 (2013) - [j10]Jian Zou, Chuancai Liu, Yue Zhang, Gui-Fu Lu:
Object recognition using Gabor co-occurrence similarity. Pattern Recognit. 46(1): 434-448 (2013) - 2012
- [j9]Gui-Fu Lu, Jian Zou, Yong Wang
Incremental learning of discriminant common vectors for feature extraction. Appl. Math. Comput. 218(22): 11269-11278 (2012) - [j8]Gui-Fu Lu, Yong Wang
Feature extraction using a fast null space based linear discriminant analysis algorithm. Inf. Sci. 193: 72-80 (2012) - [j7]Gui-Fu Lu, Jian Zou, Yong Wang
Incremental learning of complete linear discriminant analysis for face recognition. Knowl. Based Syst. 31: 19-27 (2012) - [j6]Gui-Fu Lu, Zhong Jin, Jian Zou:
Face recognition using discriminant sparsity neighborhood preserving embedding. Knowl. Based Syst. 31: 119-127 (2012) - [j5]Gui-Fu Lu, Jian Zou:
Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding. Neural Process. Lett. 35(2): 159-175 (2012) - [j4]Gui-Fu Lu, Jian Zou, Yong Wang
Incremental complete LDA for face recognition. Pattern Recognit. 45(7): 2510-2521 (2012) - 2011
- [j3]Jian Zou, Chuancai Liu, Yue Zhang, Gui-Fu Lu:
Face recognition using multi-scale differential invariants in statistical manifold framework. Int. J. Data Min. Model. Manag. 3(4): 361-374 (2011) - [j2]Gui-Fu Lu, Zhong Lin, Zhong Jin:
Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition. Neural Process. Lett. 33(3): 235-250 (2011) - 2010
- [j1]Gui-Fu Lu, Zhong Lin, Zhong Jin:
Face recognition using discriminant locality preserving projections based on maximum margin criterion. Pattern Recognit. 43(10): 3572-3579 (2010)
Coauthor Index
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