A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（工信部模式分析与机器智能重点实验室）
In reality, data often exhibit associations with multiple labels, making multi-label learning (MLL) become a prominent research topic. The last two decades have witnessed the success of MLL, which is indispensable from complete and accurate supervised information. However, obtaining such information in practice is always laborious and sometimes even impossible. To circumvent this dilemma, incomplete multi-label learning (InMLL) has emerged, aiming to learn from incomplete labeled data. To date, enormous InMLL works have been proposed to narrow the performance gap with complete MLL, whereas a systematic review for InMLL is still absent. In this paper, we not only attempt to fill the lacuna but also strive to pave the way for innovative research. Specifically, we retrospect the origin of InMLL, analyze the challenges of InMLL, and make a taxonomy of InMLL from the data-oriented and algorithm-oriented perspectives, respectively. Besides, we also present real applications of InMLL in various domains. More importantly, we highlight several potential future trends, including four open problems that are more in line with practice and three under-explored/unexplored techniques in addressing the challenges of InMLL, which may shed new light on developing novel research directions in the field of InMLL.
Graph Neural Networks: Taxonomy, Advances and Trends
Taiyuan University of Technology（太原理工大学）；Shanxi Spatial Information Network Engineering Technology Research Center（山西省空间信息网络工程技术研究中心）；Shanxi Intelligent Perception Engineering Research Center（山西省智能感知工程研究中心）
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Guangdong University of Technology（广东工业大学）；University of Macau（澳门大学）；Hefei University of Technology（合肥工业大学）
Mask-informed Deep Contrastive Incomplete Multi-view Clustering
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Computer Science, China University of Geosciences（中国地质大学计算机学院）
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
University of Southern Queensland（南昆士兰大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织数据61中心）；Queensland University of Technology (QUT)（昆士兰科技大学）；Information Technology University, Punjab（旁遮普信息技术大学）；Imperial College London（帝国理工学院）；University of Augsburg（奥格斯堡大学）
IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）
School of the Built Environment, Oxford Brookes University（牛津布鲁克斯大学建筑环境学院）；Faculty of Civil Engineering, Technische Universität Dresden（德累斯顿工业大学土木工程学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心弗莱贝格资源技术亥姆霍兹研究所）
Using Google Trends as a proxy for occupant behavior to predict building energy consumption
National University of Singapore (NUS)（新加坡国立大学）
近年来，大量能源数据和先进的机器学习算法的可用性，使建筑能源预测研究激增。然而，能源预测模型中的变量之一，居住者行为，是预测性能的关键，但难以测量或耗时收集每个建筑物。这项研究提出了一种方法，利用搜索量的主题（例如，教育}或Microsoft Excel）作为居住者行为和建筑物使用的代理。首先检查线性相关性，以探索电表数据与Google趋势搜索词之间的关系，从而推断建筑物占用率。基于ASHRAE Great Energy Predictor III（GEPIII）竞赛数据集，对包含这些项的趋势前后的预测误差进行了比较和分析。结果表明，高度相关的Google趋势数据可以有效地将建筑物子集的整体RMSLE错误减少到GEPIII竞赛前五名获胜团队的表现水平。特别是，在公共假日和具有站点特定时间表的日子期间，RMSLE误差减少分别减少20-30%和2- 5%。这些结果显示了使用Google趋势通过自动识别特定场地和假日时间表来改善部分建筑存量的能源预测的潜力。
