Face recognition using discriminant locality preserving projections based on maximum margin criterion. - Gui-Fu Lu; Zhong Lin; Zhong Jin (Pattern Recognit., 2010)

Face recognition using multi-scale differential invariants in statistical manifold framework. - Jian Zou 001; Chuancai Liu; Yue Zhang; Gui-Fu Lu (Int. J. Data Min. Model. Manag., 2011)

Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition. - Gui-Fu Lu; Zhong Lin; Zhong Jin (Neural Process. Lett., 2011)

Incremental learning of discriminant common vectors for feature extraction. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008 (Appl. Math. Comput., 2012)

Feature extraction using a fast null space based linear discriminant analysis algorithm. - Gui-Fu Lu; Yong Wang 008 (Inf. Sci., 2012)

Face recognition using discriminant sparsity neighborhood preserving embedding. - Gui-Fu Lu; Zhong Jin; Jian Zou 001 (Knowl. Based Syst., 2012)

Incremental learning of complete linear discriminant analysis for face recognition. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008 (Knowl. Based Syst., 2012)

Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding. - Gui-Fu Lu; Jian Zou 001 (Neural Process. Lett., 2012)

Incremental complete LDA for face recognition. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008 (Pattern Recognit., 2012)

Improved complete neighbourhood preserving embedding for face recognition. - Gui-Fu Lu; Yong Wang 008; Jian Zou 001 (IET Comput. Vis., 2013)

Complexity-reduced implementations of complete and null-space-based linear discriminant analysis. - Gui-Fu Lu; Wenming Zheng (Neural Networks, 2013)

Object recognition using Gabor co-occurrence similarity. - Jian Zou 001; Chuancai Liu; Yue Zhang; Gui-Fu Lu (Pattern Recognit., 2013)

Incremental learning from chunk data for IDR/QR. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008 (Image Vis. Comput., 2015)

Incremental maximum margin criterion based on eigenvalue decomposition updating algorithm. - Gui-Fu Lu; Jian Zou 001 (Mach. Vis. Appl., 2015)

Spare L1-norm-based maximum margin criterion. - Gui-Fu Lu; Ganyi Tang; Jian Zou 001 (J. Vis. Commun. Image Represent., 2016)

Generalizing intersection kernel support vector machines for color texture based recognition. - Jian Zou 001; Gui-Fu Lu; Yue Zhang; Chuancai Liu (J. Vis. Commun. Image Represent., 2016)

Graph Maximum Margin Criterion for Face Recognition. - Gui-Fu Lu; Yong Wang 008; Jian Zou 001 (Neural Process. Lett., 2016)

A New and Fast Implementation of Orthogonal LDA Algorithm and Its Incremental Extension. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008 (Neural Process. Lett., 2016)

L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008 (Pattern Recognit., 2016)

L1-norm-based principal component analysis with adaptive regularization. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008; Zhongqun Wang (Pattern Recognit., 2016)

Low-Rank Matrix Factorization With Adaptive Graph Regularizer. - Gui-Fu Lu; Yong Wang 008; Jian Zou 001 (IEEE Trans. Image Process., 2016)

L1-norm based null space discriminant analysis. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008; Zhongqun Wang (Multim. Tools Appl., 2017)

Robust Image Regression Based on the Extended Matrix Variate Power Exponential Distribution of Dependent Noise. - Lei Luo 001; Jian Yang 003; Jianjun Qian; Ying Tai; Gui-Fu Lu (IEEE Trans. Neural Networks Learn. Syst., 2017)

Sparse L1-norm-based linear discriminant analysis. - Gui-Fu Lu; Jian Zou 001; Yong Wang 008; Zhongqun Wang (Multim. Tools Appl., 2018)

Matrix exponential based discriminant locality preserving projections for feature extraction. - Gui-Fu Lu; Yong Wang 008; Jian Zou 001; Zhongqun Wang (Neural Networks, 2018)

A Fast Rough Mode Decision Algorithm for HEVC. - Wei-Xin Yao; Dan Yang; Gui-Fu Lu; Jun Wang (J. Inf. Process. Syst., 2019)

Using Algebra Graph Representation to Detect Pairwise-Constraint Software Faults. - Yong Wang 008; Xiangyu Chen; Wan Zhou; Xuejuan Liu; Jun Li; Gui-Fu Lu (IEEE Access, 2020)

Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint. - Gui-Fu Lu; Qin-Ru Yu; Yong Wang 008; Ganyi Tang (Neural Networks, 2020)

Improved Non-negative Matrix Factorization Algorithm for Sparse Graph Regularization. - Caifeng Yang; Tao Liu; Gui-Fu Lu; Zhenxin Wang; Zhi Deng (ICPCSEE (1), 2021)

Block Diagonal Sparse Subspace Clustering. - Lili Fan; Gui-Fu Lu; Yong Wang 008; Tao Liu (WCSP, 2021)

Multi-view subspace clustering with Kronecker-basis-representation-based tensor sparsity measure. - Gui-Fu Lu; Hua Li; Yong Wang 008; Ganyi Tang (Mach. Vis. Appl., 2021)

δ-Norm-Based Robust Regression With Applications to Image Analysis. - Shuo Chen 003; Jian Yang 003; Yang Wei; Lei Luo 001; Gui-Fu Lu; Chen Gong 002 (IEEE Trans. Cybern., 2021)

Kernel Subspace Clustering based on Block Diagonal Representation and Sparse Constraints. - Lili Fan; Gui-Fu Lu; Ganyi Tang; Yong Wang 008 (DSA, 2022)

Constrained Multilinear Multi-view Subspace Representation Learning for Clustering based on Tensor Nuclear Norm. - Rong Tang 005; Gui-Fu Lu (DSA, 2022)

Robust low-rank representation with adaptive graph regularization from clean data. - Gui-Fu Lu; Yong Wang 008; Ganyi Tang (Appl. Intell., 2022)

Latent multi-view self-representations for clustering via the tensor nuclear norm. - Gui-Fu Lu; Jinbiao Zhao 001 (Appl. Intell., 2022)

Clean and robust affinity matrix learning for multi-view clustering. - Jin-Biao Zhao 001; Gui-Fu Lu (Appl. Intell., 2022)

Tensor subspace clustering using consensus tensor low-rank representation. - Bing Cai; Gui-Fu Lu (Inf. Sci., 2022)

One-step incomplete multiview clustering with low-rank tensor graph learning. - Guangyan Ji; Gui-Fu Lu (Inf. Sci., 2022)

Double structure scaled simplex representation for multi-view subspace clustering. - Liang Yao; Gui-Fu Lu (Neural Networks, 2022)

Efficient Tensor Low-Rank Representation with a Closed Form Solution. - Yaozu Kan; Gui-Fu Lu; Yangfan Du; Guangyan Ji (ACPR (2), 2023)

Scalable incomplete multi-view clustering via tensor Schatten p-norm and tensorized bipartite graph. - Guangyan Ji; Gui-Fu Lu; Bing Cai (Eng. Appl. Artif. Intell., 2023)

Robust and optimal neighborhood graph learning for multi-view clustering. - Yangfan Du; Gui-Fu Lu; Guangyan Ji (Inf. Sci., 2023)

Consensus similarity learning based on tensor nuclear norm. - Rong Tang 005; Gui-Fu Lu (Mach. Vis. Appl., 2023)

High-order manifold regularized multi-view subspace clustering with robust affinity matrices and weighted TNN. - Bing Cai; Gui-Fu Lu; Liang Yao; Hua Li (Pattern Recognit., 2023)

Clean affinity matrix learning with rank equality constraint for multi-view subspace clustering. - Jinbiao Zhao 001; Gui-Fu Lu (Pattern Recognit., 2023)

Mixed structure low-rank representation for multi-view subspace clustering. - Shouhang Wang; Yong Wang 008; Gui-Fu Lu; Wenge Le (Appl. Intell., 2023)

Multi-view clustering indicator learning with scaled similarity. - Liang Yao; Gui-Fu Lu (Pattern Anal. Appl., 2023)

Unbalanced incomplete multi-view clustering based on low-rank tensor graph learning. - Guangyan Ji; Gui-Fu Lu; Bing Cai; Yangfan Du (Expert Syst. Appl., 2023)

A late fusion scheme for multi-graph regularized NMF. - Guangyan Ji; Gui-Fu Lu (Mach. Vis. Appl., 2023)

Multi-view clustering based on a multimetric matrix fusion method. - Liang Yao; Gui-Fu Lu; JinBiao Zhao 001; Bing Cai (Expert Syst. Appl., 2023)

Robust subspace clustering via multi-affinity matrices fusion. - Yangfan Du; Gui-Fu Lu; Guangyan Ji; Jinhua Liu (Knowl. Based Syst., 2023)

Consensus latent incomplete multi-view clustering with low-rank tensor constraint. - Guangyan Ji; Gui-Fu Lu (Int. J. Mach. Learn. Cybern., 2023)

Multi-view subspace similarity learning based on t-SVD. - Rong Tang 005; Gui-Fu Lu (Multim. Tools Appl., 2023)

Multi-view clustering using a flexible and optimal multi-graph fusion method. - Yaozu Kan; Gui-Fu Lu; Liang Yao; Bing Cai; JinBiao Zhao 001 (Eng. Appl. Artif. Intell., 2024)

Tensor-based global block-diagonal structure radiation for incomplete multiview clustering. - Xiaoxing Guo; Gui-Fu Lu (Expert Syst. Appl., 2024)

Coupled double self-expressive subspace clustering with low-rank tensor learning. - Tong Wu; Gui-Fu Lu (Expert Syst. Appl., 2024)

Aligned multi-view clustering for unmapped data via weighted tensor nuclear norm and adaptive graph learning. - Bing Cai; Gui-Fu Lu; Liang Yao; Jiashan Wan (Neurocomputing, 2024)

Complete multi-view subspace clustering via auto-weighted combination of visible and latent views. - Bing Cai; Gui-Fu Lu; Guangyan Ji; Weihong Song (Inf. Sci., 2024)

Comprehensive consensus representation learning for incomplete multiview subspace clustering. - Xiaoxing Guo; Gui-Fu Lu (Inf. Sci., 2024)

Coupled double consensus multi-graph fusion for multi-view clustering. - Tong Wu; Gui-Fu Lu (Inf. Sci., 2024)

An adaptive kernel dictionary-based low-rank representation method for subspace clustering. - Yaozu Kan; Gui-Fu Lu; Yangfan Du (Neural Networks, 2024)

Robust Least Squares Regression for Subspace Clustering: A Multi-View Clustering Perspective. - Yangfan Du; Gui-Fu Lu; Guangyan Ji (IEEE Trans. Image Process., 2024)

Tensorized Scaled Simplex Representation for Multi-View Clustering. - Bing Cai; Gui-Fu Lu; Hua Li; Weihong Song (IEEE Trans. Multim., 2024)

A fast anchor-based graph-regularized low-rank representation approach for large-scale subspace clustering. - Lili Fan; Gui-Fu Lu; Ganyi Tang; Yong Wang 008 (Mach. Vis. Appl., 2024)

Auto-weighted multi-view clustering with the use of an augmented view. - Bing Cai; Gui-Fu Lu; Jiashan Wan; Yangfan Du (Signal Process., 2024)

Tensor low-rank representation combined with consistency and diversity exploration. - Yaozu Kan; Gui-Fu Lu; Guangyan Ji; Yangfan Du (Int. J. Mach. Learn. Cybern., 2024)

Tensor idempotent representation with auto-weighted exponential tensor nuclear norm minimization for multi-view clustering. - Tong Wu; Gui-Fu Lu (Appl. Soft Comput., 2025)

Multi-view clustering via view-specific consensus kernelized graph learning. - Bing Hu; Tong Wu; Lixin Han; Shu Li; Yi Xu; Gui-Fu Lu (Neurocomputing, 2025)

Auto-weighted exponential tensor nuclear norm minimization for multi-attribute low-dimensional subspace clustering. - Tong Wu; Gui-Fu Lu; Bing Hu (Neurocomputing, 2025)

Tensorized diversity and consistency with Laplacian manifold for multi-view clustering. - Tong Wu; Gui-Fu Lu (Inf. Sci., 2025)

Improving user-oriented fairness in recommendation via data augmentation: Don't worry about inactive users. - Yong Wang 008; Huadong Zhou; Gui-Fu Lu; Cuiyun Gao; Shuai Meng (J. Syst. Softw., 2025)

Tensor multi-subspace learning for robust tensor-based multi-view clustering. - Bing Cai; Gui-Fu Lu; Guangyan Ji; Yangfan Du (Knowl. Based Syst., 2025)

Unlocking deep structures: Anchor-side filtering for efficient multiview clustering on high-order bipartite graphs. - Fei Wang 106; Gui-Fu Lu (Knowl. Based Syst., 2025)

Structure regularized consensus dynamic anchor graph learning for incomplete multi-view clustering. - Bing Hu; Lixin Han; Yi Xu; Chang Tang; Jun Zhu; Gui-Fu Lu (Neural Networks, 2025)

Scalable one-pass multi-view clustering with tensorized multiscale bipartite graphs fusion. - Fei Wang 106; Gui-Fu Lu (Neural Networks, 2025)

Tensorized latent representation with automatic dimensionality selection for multi-view clustering. - Bing Cai; Gui-Fu Lu; Xiaoxing Guo; Tong Wu (Pattern Recognit., 2025)

Joint local smoothness and low-rank tensor representation for robust multi-view clustering. - Yangfan Du; Gui-Fu Lu (Pattern Recognit., 2025)

Tensor-based incomplete multiple kernel clustering with auto-weighted late fusion alignment. - Xiaoxing Guo; Gui-Fu Lu (Pattern Recognit., 2025)

Consensus Incomplete Multi-View Clustering with Nonconvex Tensor Smooth Graph Learning. - Shanshan Wu; Gui-Fu Lu (Pattern Recognit., 2025)

Specific and coupled double consistency multi-view subspace clustering with low-rank tensor learning. - Tong Wu; Gui-Fu Lu (Signal Process., 2025)