Lu Sun
@shanghaitech.edu.cn
Assistant Professor, School of Information Science and Technology
ShanghaiTech University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Lu Sun and Mineichi Kudo
Springer Science and Business Media LLC
Lu Sun, Canh Hao Nguyen, and Hiroshi Mamitsuka
International Joint Conferences on Artificial Intelligence Organization
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.
Lu Sun, Canh Hao Nguyen, and Hiroshi Mamitsuka
International Joint Conferences on Artificial Intelligence Organization
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.
Lu Sun and Mineichi Kudo
Elsevier BV
Lu SUN, Mineichi KUDO, and Keigo KIMURA
Institute of Electronics, Information and Communications Engineers (IEICE)
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
Batzaya Norov-Erdene, Mineichi Kudo, Lu Sun, and Keigo Kimura
IEEE
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.
Keigo Kimura, Mineichi Kudo, Lu Sun, and Sadamori Koujaku
IEEE
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.
Lu Sun, Mineichi Kudo, and Keigo Kimura
IEEE
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.
Lu Sun, Mineichi Kudo and Keigo Kimura
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.
Keigo Kimura, Mineichi Kudo, and Lu Sun
Springer International Publishing