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Collaboration Based Multi-Label Learning

Lei Feng, Bo An, Shuo He
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
It is well-known that exploiting label correlations is crucially important to multi-label learning.  ...  Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space.  ...  Based on this assumption, a novel multi-label learning approach named CAMEL, i.e., CollAboration based Multi-labEl Learning, is proposed.  ... 
doi:10.1609/aaai.v33i01.33013550 fatcat:bjad4i457bepva2hco53ovihte

Collaboration based Multi-Label Learning [article]

Lei Feng, Bo An, Shuo He
2019 arXiv   pre-print
It is well-known that exploiting label correlations is crucially important to multi-label learning.  ...  Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space.  ...  Based on this assumption, a novel multi-label learning approach named CAMEL, i.e., CollAboration based Multi-labEl Learning, is proposed.  ... 
arXiv:1902.03047v1 fatcat:kltrocwjrzh37bb27ztuwos4uy

Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization [article]

Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang and Maozu Guo
2019 arXiv   pre-print
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels.  ...  In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf.  ...  Conclusion In this paper, we proposed a collaborative matrix factorization based multi-view multi-instance multi-label learning approach called M3Lcmf.  ... 
arXiv:1905.05061v2 fatcat:rhkqgigc3rbrpgzf2iaetdlepq

Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization

Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, Maozu Guo
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels.  ...  .\ In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf.  ...  This investigation justifies our motivation Conclusion In this paper, we proposed a collaborative matrix factorization based multi-view multi-instance multi-label learning approach called M3Lcmf.  ... 
doi:10.1609/aaai.v33i01.33015508 fatcat:jszbhnadbrbmtckzdj3hd26ucy

Comprehensive study and Analysis of Extreme Multi-Label Classification Approach

Purvi Prajapati
2020 International Journal of Advanced Trends in Computer Science and Engineering  
This paper discussed different approaches for large scale Recommendation System using Extreme Multi-Label Classification Approach and empirical evaluation carried out on three multi-label datasets which  ...  In Recommendation System, the main goal is to recommend users based on the available data.  ...  XML-CNN [11] is the deep learning based CNN (Convolutional Neural Network) for extreme multi-label data.  ... 
doi:10.30534/ijatcse/2020/83922020 fatcat:qzgi7mtk4bfnbk4dmdjblp373y

Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors

Nesma Settouti, Khalida Douibi, Mohammed El Amine Bechar, Mostafa El Habib Daho, Meryem Saidi
2019 Open Computer Science  
In this paper, a Collaborative Bagged Multi-label K-Nearest-Neighbors (CobMLKNN) algorithm is proposed, that extend the co-Training paradigm by a Multi-label K-Nearest-Neighbors algorithm.  ...  The manual annotation of available datasets is time-consuming and need a huge effort from the expert, especially for Multi-label applications in which each example of learning is associated with many labels  ...  Conclusion In this work, we studied the problem of semi-supervised learning for a Multi-label framework. We proposed collaborative Multi-label learning using an ensemble learning named CobMLKNN.  ... 
doi:10.1515/comp-2019-0017 fatcat:24kgnww2mnattcnbjl25f5osda

Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation [article]

Chaowei Tan, Zhennan Yan, Shaoting Zhang, Kang Li, Dimitris N. Metaxas
2019 arXiv   pre-print
The key contribution is the adversarial learning based collaborative multi-agent segmentation network.  ...  The collaborative learning is driven by an adversarial sub-network.  ...  (c) demonstrates the cartilage labels in 3D. Fig. 2 . 2 Flowchart of the collaborative multi-agent learning for cartilage segmentation.  ... 
arXiv:1908.04469v1 fatcat:gwlpezkk3vfehkwbzuzzbwnlsm

Stylometric Authorship Attribution of Collaborative Documents [chapter]

Edwin Dauber, Rebekah Overdorf, Rachel Greenstadt
2017 Lecture Notes in Computer Science  
Based on the results of these experiments and knowledge of the multi-label classifiers used, we propose a hypothesis to explain this overall poor performance.  ...  Additionally, we perform authorship attribution of pre-segmented text from the Wikia dataset, and show that while this performs better than multi-label learning it requires large amounts of data to be  ...  Background and Related Work Multi-Label Learning There have been a number of proposed techniques for multi-label learning that we consider in this work.  ... 
doi:10.1007/978-3-319-60080-2_9 fatcat:usmbstcxsna3zadtwwfrmbkltq

CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration [article]

Congcong Ge, Pengfei Wang, Lu Chen, Xiaoze Liu, Baihua Zheng, Yunjun Gao
2021 arXiv   pre-print
CollaborER consists of two phases, i.e., automatic label generation (ALG) and collaborative ER training (CERT).  ...  In this paper, we present CollaborER, a self-supervised entity resolution framework via multi-features collaboration.  ...  CERT is composed of two phases, i.e., (i) multi-relational graph feature learning (MRGFL) and (ii) collaborative sentence feature learning (CSFL).  ... 
arXiv:2108.08090v2 fatcat:xcwnwpodanh5tjgwcgxikjkdfq

Activity-edge centric multi-label classification for mining heterogeneous information networks

Yang Zhou, Ling Liu
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
Second, we utilize the label vicinity to capture the pairwise vertex closeness based on the labeling on the activity-based collaboration multigraph.  ...  Multi-label classification of heterogeneous information networks has received renewed attention in social network analysis.  ...  This material is based upon work partially supported by the NSF under Grants IIS-0905493, CNS-1115375, IIP-1230740, and a grant from Intel ISTC on Cloud Computing.  ... 
doi:10.1145/2623330.2623737 dblp:conf/kdd/ZhouL14 fatcat:nkfhap7gtbgmhkyjum3gsfzpii

Semi-supervised Multi-label Learning by Solving a Sylvester Equation [chapter]

Gang Chen, Yangqiu Song, Fei Wang, Changshui Zhang
2008 Proceedings of the 2008 SIAM International Conference on Data Mining  
Multi-label learning refers to the problems where an instance can be assigned to more than one category.  ...  In this paper, we present a novel Semi-supervised algorithm for Multi-label learning by solving a Sylvester Equation (SMSE).  ...  This encourages us to propose the following graph-based semi-supervised algorithm for multi-label learning, i.e.  ... 
doi:10.1137/1.9781611972788.37 dblp:conf/sdm/ChenSWZ08 fatcat:rwimejcpzjbgdhj4rducybsehu

A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels [article]

Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Tristan Kreuziger, Begum Demir
2021 arXiv   pre-print
To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method.  ...  The group lasso module detects the potentially noisy labels by estimating the label uncertainty based on the aggregation of two collaborative networks.  ...  CONCLUSION In this paper, a novel Consensual Collaborative Multi-label Learning (CCML) method has been presented.  ... 
arXiv:2105.05496v2 fatcat:f4cwlafib5durlna62i27mezyq

Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation [article]

Jianzhong He, Xu Jia, Shuaijun Chen, Jianzhuang Liu
2021 arXiv   pre-print
In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation.  ...  Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain.  ...  Conclusion In this paper, we present an effective multi-source domain adaptation framework for semantic segmentation based on collaborative learning.  ... 
arXiv:2103.04717v3 fatcat:7jidp4uyjrattbfpdj4nu4ehqy

An Attention-based Collaboration Framework for Multi-View Network Representation Learning [article]

Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han
2017 arXiv   pre-print
We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations.  ...  Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications.  ...  To the best of our knowledge, this is the rst e ort to adopt the a ention-based approach in the problem of multi-view network representation learning.  ... 
arXiv:1709.06636v1 fatcat:uk5memuwrbe5jfa5jh5c2imwlu

Multi-Label Noise Robust Collaborative Learning Method for Remote Sensing Image Classification [article]

Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Begüm Demir
2022 arXiv   pre-print
To address this problem, we propose a novel multi-label noise robust collaborative learning (RCML) method to alleviate the negative effects of multi-label noise during the training phase of a CNN model  ...  However, multi-label noise (which can be associated with wrong and missing label annotations) can distort the learning process of the MLC methods.  ...  the European Research Council (ERC) through the ERC-2017-STG BigEarth Project under Grant 759764 and by the German Ministry for Education and Research as BIFOLD -Berlin Institute for the Foundations of Learning  ... 
arXiv:2012.10715v5 fatcat:26g4qfytafh67huj4lrnzrqfeq
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