Lu Sun

@shanghaitech.edu.cn

Assistant Professor, School of Information Science and Technology
ShanghaiTech University

Lu Sun


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https://researchid.co/lusun850912

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence
12

Scopus Publications

316

Scholar Citations

11

Scholar h-index

13

Scholar i10-index

Scopus Publications

  • Multi-label classification by polytree-augmented classifier chains with label-dependent features
    Lu Sun, Mineichi Kudo
    Pattern Analysis and Applications, 2019
  • Fast and robust multi-view multi-task learning via group sparsity
    Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka
    Ijcai International Joint Conference on Artificial Intelligence, 2019
    Multi-view multi-task learning has recently attracted more and more attention due to its dual-heterogeneity, i.e.,each task has heterogeneous features from multiple views, and probably correlates with other tasks via common views.Existing methods usually suffer from three problems: 1) lack the ability to eliminate noisy features, 2) hold a strict assumption on view consistency and 3) ignore the possible existence of task-view outliers.To overcome these limitations, we propose a robust method with joint group-sparsity by decomposing feature parameters into a sum of two components,in which one saves relevant features (for Problem 1) and flexible view consistency (for Problem 2),while the other detects task-view outliers (for Problem 3).With a global convergence property, we develop a fast algorithm to solve the optimization problem in a linear time complexity w.r.t. the number of features and labeled samples.Extensive experiments on various synthetic and real-world datasets demonstrate its effectiveness.
  • Multiplicative sparse feature decomposition for efficient multi-view multi-task learning
    Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka
    Ijcai International Joint Conference on Artificial Intelligence, 2019
    Multi-view multi-task learning refers to dealing with dual-heterogeneous data,where each sample has multi-view features,and multiple tasks are correlated via common views.Existing methods do not sufficiently address three key challenges:(a) saving task correlation efficiently, (b) building a sparse model and (c) learning view-wise weights.In this paper, we propose a new method to directly handle these challenges based on multiplicative sparse feature decomposition.For (a), the weight matrix is decomposed into two components via low-rank constraint matrix factorization, which saves task correlation by learning a reduced number of model parameters.For (b) and (c), the first component is further decomposed into two sub-components,to select topic-specific features and learn view-wise importance, respectively. Theoretical analysis reveals its equivalence with a general form of joint regularization,and motivates us to develop a fast optimization algorithm in a linear complexity w.r.t. the data size.Extensive experiments on both simulated and real-world datasets validate its efficiency.
  • Optimization of classifier chains via conditional likelihood maximization
    Lu Sun, Mineichi Kudo
    Pattern Recognition, 2018
  • READER: Robust semi-supervised multi-label dimension reduction
    Lu SUN, Mineichi KUDO, Keigo KIMURA
    IEICE Transactions on Information and Systems, 2017
    Multi-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several problems, such as ignoring label outliers and label correlations. In addition, most of them emphasize on conducting dimension reduction in an unsupervised or supervised way, therefore, unable to utilize the label information or a large amount of unlabeled data to improve the performance. In order to cope with these problems, we propose a novel method termed Robust sEmi-supervised multi-lAbel DimEnsion Reduction, shortly READER. From the viewpoint of empirical risk minimization, READER selects most discriminative features for all the labels in a semi-supervised way. Specifically, the 2,1-norm induced loss function and regularization term make READER robust to the outliers in the data points. READER finds a feature subspace so as to keep originally neighbor instances close and embeds labels into a low-dimensional latent space nonlinearly. To optimize the objective function, an efficient algorithm is developed with convergence property. Extensive empirical studies on real-world datasets demonstrate the superior performance of the proposed method. key words: multi-label classification, semi-supervised dimension reduction, risk minimization, feature selection, manifold learning
  • Multi-label classification with meta-label-specific features
    Lu Sun, Mineichi Kudo, Keigo Kimura
    Proceedings International Conference on Pattern Recognition, 2016
    Multi-label classification has attracted many attentions in various fields, such as text categorization and semantic image annotation. Aiming to classify an instance into multiple labels, various multi-label classification methods have been proposed. However, the existing methods typically build models in the identical feature (sub)space for all labels, possibly inconsistent with real-world problems. In this paper, we develop a novel method based on the assumption that meta-labels with specific features exist in the scenario of multi-label classification. The proposed method consists of meta-label learning and specific feature selection. Experiments on twelve benchmark multi-label datasets show the efficiency of the proposed method compared with several state-of-the-art methods.
  • A scalable clustering-based local multi-label classification method
    Lu Sun, Mineichi Kudo, Keigo Kimura
    Frontiers in Artificial Intelligence and Applications, 2016
    Multi-label classification aims to assign multiple labels to a single test instance. Recently, more and more multi-label classification applications arise as large-scale problems, where the numbers of instances, features and labels are either or all large. To tackle such problems, in this paper we develop a clustering-based local multi-label classification method, attempting to reduce the problem size in instances, features and labels. Our method consists of lowdimensional data clustering and local model learning. Specifically, the original dataset is firstly decomposed into several regular-scale parts by applying clustering analysis on the feature subspace, which is induced by a supervised multi-label dimension reduction technique; then, an efficient local multi-label model, meta-label classifier chains, is trained on each data cluster. Given a test instance, only the local model belonging to the nearest cluster to it is activated to make the prediction. Extensive experiments performed on eighteen benchmark datasets demonstrated the efficiency of the proposed method compared with the state-of-the-art algorithms.
  • Simultaneous nonlinear label-instance embedding for multi-label classification
    Keigo Kimura, Mineichi Kudo, Lu Sun
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
  • Locality in multi-label classification problems
    Batzaya Norov-Erdene, Mineichi Kudo, Lu Sun, Keigo Kimura
    Proceedings International Conference on Pattern Recognition, 2016
    Lately, multi-label classification (MLC) problems have drawn a lot of attention in a wide range of fields including medical, web, and entertainment. The scale and the diversity of MLC problems is much larger than single-label classification problems. Especially we have to face all possible combinations of labels. To solve MLC problems more efficiently, we focus on three kinds of locality hidden in a given MLC problem. In this paper, first we show how large degree of locality exists in nine datasets, then examine how closely they are related to labels, and last propose a method of reducing the problem size using one kind of locality.
  • Fast random k-labELsets for large-scale multi-label classification
    Keigo Kimura, Mineichi Kudo, Lu Sun, Sadamori Koujaku
    Proceedings International Conference on Pattern Recognition, 2016
    Multi-label classification (MLC), allowing instances to have multiple labels, has been received a surge of interests in recent years due to its wide range of applications such as image annotation and document tagging. One of simplest ways to solve MLC problems is label-power set method (LP) that regards all possible label subsets as classes. LP validates traditional multi-classification classifiers such as multi-class SVM but it suffers from the increased number of classes. Therefore, several improvements have been made for LP to be scaled for large problems with many labels. Random k labELsets (RAkEL) proposed by Tsoumakas et al. solves this problem by randomly sampling a small number of labels and taking ensemble of them. However, RAkEL needs all instances for constructing each model and thus suffers from high computational complexity. In this paper, we propose a new fast algorithm for RAkEL. First, we assign each training instance to a small number of models. Then LP is applied for each model with only the assigned instances. Experiments on twelve benchmark datasets demonstrated that the proposed algorithm works faster than the conventional methods while keeping accuracy. In the best case, it was 100 times faster than baseline method (LP) and 30 times faster than the original RAkEL.
  • Novel fast license plate location method based on hierarchical strategy
    Jilin Daxue Xuebao Gongxueban Journal of Jilin University Engineering and Technology Edition, 2015
  • Polytree-augmented classifier chains for multi-label classification
    Ijcai International Joint Conference on Artificial Intelligence, 2015

RECENT SCHOLAR PUBLICATIONS

  • Multi-view multi-label personalized classification via generalized exclusive sparse tensor factorization: L. Fei et al.
    L Fei, W Lin, J Wang, L Sun, M Kudo, K Kimura
    Knowledge and Information Systems 67 (9), 8023-8057 , 2025
    2025
  • Multi-level sparse network lasso: Locally sparse learning with flexible sample clusters
    L Fei, X Wang, J Wang, L Sun, Y Zhang
    Neurocomputing 635, 129898 , 2025
    2025
    Citations: 1
  • Robust multi-view subspace clustering with missing data by aligning nonlinear manifolds
    ZW Mao, L Sun, Y Wu
    Pattern Recognition 161, 111280 , 2025
    2025
    Citations: 10
  • Multi-level network Lasso for multi-task personalized learning
    J Wang, L Fei, L Sun
    Pattern Recognition 161, 111213 , 2025
    2025
    Citations: 4
  • Mome: Mixture-of-masked-experts for efficient multi-task recommendation
    J Xu, L Sun, D Zhao
    Proceedings of the 47th International ACM SIGIR Conference on Research and … , 2024
    2024
    Citations: 30
  • Redirected transfer learning for robust multi-layer subspace learning
    J Bao, M Kudo, K Kimura, L Sun
    Pattern Analysis and Applications 27 (1), 25 , 2024
    2024
    Citations: 7
  • Learning Compact Neural Networks via Generalized Structured Sparsity
    K Bian, L Sun, D Zhao
    ECAI 2024: 27th European Conference on Artificial Intelligence, 19–24 … , 2024
    2024
  • Learning low-rank tensor cores with probabilistic l0-regularized rank selection for model compression
    T Cao, L Sun, CH Nguyen, H Mamitsuka
    Proceedings of the 33rd International Joint Conference on Artificial … , 2024
    2024
    Citations: 8
  • Robust embedding regression for semi-supervised learning
    J Bao, M Kudo, K Kimura, L Sun
    Pattern Recognition 145, 109894 , 2024
    2024
    Citations: 28
  • Multi-label personalized classification via exclusive sparse tensor factorization
    W Lin, J Wang, L Sun, M Kudo, K Kimura
    2023 IEEE international conference on data mining (ICDM), 398-407 , 2023
    2023
    Citations: 1
  • Partial Multi-label Learning with a Few Accurately Labeled Data
    H Mizuguchi, K Kimura, M Kudo, L Sun
    Pacific Rim International Conference on Artificial Intelligence, 79-90 , 2023
    2023
  • Incomplete multi-view weak-label learning with noisy features and imbalanced labels
    Z Li, Z Yang, L Sun, M Kudo, K Kimura
    Pacific Rim International Conference on Artificial Intelligence, 124-130 , 2023
    2023
    Citations: 3
  • Grouped Multi-Task Learning with Hidden Tasks Enhancement.
    J Jin, J Wang, L Sun, J Zheng, M Kudo
    ECAI, 1164-1171 , 2023
    2023
    Citations: 1
  • Generalized Discriminative Deep Non-Negative Matrix Factorization Based on Latent Feature and Basis Learning.
    Z Yang, Z Li, L Sun
    IJCAI, 4486-4494 , 2023
    2023
    Citations: 2
  • Fed-SC: one-shot federated subspace clustering over high-dimensional data
    S Xie, Y Wu, K Liao, L Chen, C Liu, H Shen, MJ Tang, L Sun
    2023 IEEE 39th International Conference on Data Engineering (ICDE), 2905-2918 , 2023
    2023
    Citations: 13
  • Retargeted Regression Methods for Multi-label
    K Kimural, J Bao¹, M Kudo¹, L Sun
    Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR … , 2023
    2023
  • of Kernelized Implicit Mappings
    M Kudo, KKS Morishita¹, L Sun
    Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR … , 2023
    2023
  • Structured sparse multi-task learning with generalized group lasso
    L Fei, L Sun, M Kudo, K Kimura
    ECAI 2023: 26th European Conference on Artificial Intelligence, September 30 … , 2023
    2023
    Citations: 2
  • Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning
    X Wang, L Sun, CH Nguyen, H Mamitsuka
    ECAI 2023: 26th European Conference on Artificial Intelligence, September 30 … , 2023
    2023
    Citations: 1
  • Retargeted Regression Methods for Multi-label Learning
    K Kimura, J Bao, M Kudo, L Sun
    Joint IAPR International Workshops on Statistical Techniques in Pattern … , 2022
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • Multi-label classification with meta-label-specific features
    L Sun, M Kudo, K Kimura
    2016 23rd International conference on pattern recognition (ICPR), 1612-1617 , 2016
    2016
    Citations: 49
  • Mome: Mixture-of-masked-experts for efficient multi-task recommendation
    J Xu, L Sun, D Zhao
    Proceedings of the 47th International ACM SIGIR Conference on Research and … , 2024
    2024
    Citations: 30
  • Robust embedding regression for semi-supervised learning
    J Bao, M Kudo, K Kimura, L Sun
    Pattern Recognition 145, 109894 , 2024
    2024
    Citations: 28
  • Fast random k-labelsets for large-scale multi-label classification
    K Kimura, M Kudo, L Sun, S Koujaku
    2016 23rd International Conference on Pattern Recognition (ICPR), 438-443 , 2016
    2016
    Citations: 25
  • Optimization of classifier chains via conditional likelihood maximization
    L Sun, M Kudo
    Pattern Recognition 74, 503-517 , 2018
    2018
    Citations: 20
  • Mlc toolbox: A matlab/octave library for multi-label classification
    K Kimura, L Sun, M Kudo
    arXiv preprint arXiv:1704.02592 , 2017
    2017
    Citations: 20
  • Multi-label classification by polytree-augmented classifier chains with label-dependent features
    L Sun, M Kudo
    Pattern analysis and applications 22 (3), 1029-1049 , 2019
    2019
    Citations: 17
  • Fed-SC: one-shot federated subspace clustering over high-dimensional data
    S Xie, Y Wu, K Liao, L Chen, C Liu, H Shen, MJ Tang, L Sun
    2023 IEEE 39th International Conference on Data Engineering (ICDE), 2905-2918 , 2023
    2023
    Citations: 13
  • READER: robust semi-supervised multi-label dimension reduction
    L Sun, M Kudo, K Kimura
    IEICE TRANSACTIONS on Information and Systems 100 (10), 2597-2604 , 2017
    2017
    Citations: 13
  • Simultaneous nonlinear label-instance embedding for multi-label classification
    K Kimura, M Kudo, L Sun
    Joint IAPR international workshops on statistical techniques in pattern … , 2016
    2016
    Citations: 12
  • Polytree-Augmented Classifier Chains for Multi-Label Classification.
    L Sun, M Kudo
    IJCAI, 3834-3840 , 2015
    2015
    Citations: 11
  • Robust multi-view subspace clustering with missing data by aligning nonlinear manifolds
    ZW Mao, L Sun, Y Wu
    Pattern Recognition 161, 111280 , 2025
    2025
    Citations: 10
  • A Scalable Clustering-Based Local Multi-Label Classification Method.
    L Sun, M Kudo, K Kimura
    ECAI, 261-268 , 2016
    2016
    Citations: 10
  • Multi-Task Personalized Learning with Sparse Network Lasso.
    J Wang, L Sun
    IJCAI, 3516-3522 , 2022
    2022
    Citations: 9
  • Learning low-rank tensor cores with probabilistic l0-regularized rank selection for model compression
    T Cao, L Sun, CH Nguyen, H Mamitsuka
    Proceedings of the 33rd International Joint Conference on Artificial … , 2024
    2024
    Citations: 8
  • Redirected transfer learning for robust multi-layer subspace learning
    J Bao, M Kudo, K Kimura, L Sun
    Pattern Analysis and Applications 27 (1), 25 , 2024
    2024
    Citations: 7
  • Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning.
    L Sun, CH Nguyen, H Mamitsuka
    IJCAI, 3506-3512 , 2019
    2019
    Citations: 7
  • Dimension reduction using nonnegative matrix tri-factorization in multi-label classification
    K Kimura, M Kudo, L Sun
    Proceedings of the international conference on parallel and distributed … , 2015
    2015
    Citations: 5
  • Multi-level network Lasso for multi-task personalized learning
    J Wang, L Fei, L Sun
    Pattern Recognition 161, 111213 , 2025
    2025
    Citations: 4
  • Incomplete multi-view weak-label learning with noisy features and imbalanced labels
    Z Li, Z Yang, L Sun, M Kudo, K Kimura
    Pacific Rim International Conference on Artificial Intelligence, 124-130 , 2023
    2023
    Citations: 3