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Advancing robust regression: Addressing asymmetric noise with the BLINEX loss function

INFORMATION FUSION 2024年 110卷

作者： Tang, Jingjing Liu, Bangxin Fu, Saiji Tian, Yingjie Kou, Gang Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Inst Big Data Chengdu 611130 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

In real -world applications, regression performance is significantly impeded by complex noise. The choice of loss function is pivotal in building robust regression models, which broadly fall into two categories: bounded symmetric loss functions and asymmetric loss functions. The former struggle with asymmetric noise, while the latter are formulated as segmented functions involving complicates optimization. To handle various noise within a concise framework, we propose a robust regression model based on the asymmetric bounded linearexponential (BLINEX) loss function, called BXSVR. Firstly, the asymmetry of the BLINEX loss function enables BXSVR to apply different penalties to positively and negatively skewed noise with equal regression error magnitudes to balance the effect of asymmetric noise. Secondly, the boundedness of the BLINEX loss function allows BXSVR to mitigate the detrimental effects of large noise. Thirdly, the smoothness and differentiability of the BLINEX loss function contributes to the optimization of BXSVR. Due to the above properties, BSXVR can adaptively and effectively manage diverse noise scenarios. We use the Nesterov accelerated gradient (NAG) algorithm to optimize BSXVR and analyze the complexity of our algorithm. Extensive experiments on synthetic datasets, real -world datasets, and an application dataset confirm that BXSVR is more competitive than other benchmark methods in terms of various measures.

关键词： Support vector regression Asymmetric noise BLINEX loss function Robustness

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Multiple factor flows and heterogeneous premium of urban house prices: Empirical evidence from Chinese cities

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Finance Research Letters 2024年 69卷

作者： Huang, Xirong Geng, Chen Li, Xiuting Dong, Jichang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences es Beijing 100190 China

Notable Regional heterogeneity is commonly observed in housing markets. This study examines how multiple factor flows affect urban house prices and the resulting premium. It utilizes over 100 million pieces of Chinese intercity factor flow data to construct a multi-layer network and introduces a new metric called multiple connectivity quality to measure a city's comprehensive factor connection capability. Empirical analysis reveals that cities with higher multiple connectivity quality tend to exhibit higher price premiums, making them more resilient to market fluctuations. This study offers fresh insight into regional heterogeneity within housing markets and holds practical significance for stabilizing these markets. © 2024 The Author(s)

关键词： Heterogeneous premiums House prices Multi-layer network Multiple factor flows Panel data model

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Incomplete multi-view learning: Review, analysis, and prospects

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APPLIED SOFT COMPUTING 2024年 153卷

作者： Tang, Jingjing Yi, Qingqing Fu, Saiji Tian, Yingjie Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Inst Big Data Chengdu 611130 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

Multi -view data, stemming from diverse information sources, often suffer from incompleteness due to various factors such as equipment failure and data transmission issues. This challenge has given rise to the emerging field of incomplete multi -view learning (IML). To provide guidance for newcomers and researchers in this field, this survey systematically presents an in-depth analysis of IML from generative and discriminative perspectives, focusing on all missing scenarios and various learning tasks. Within these categories, discriminative methods are further classified into matrix learning -based IML and graph learning -based IML, while generative methods encompass generative adversarial networks -based IML, auto -encoder -based IML, and contrastive learning -based IML. Meanwhile, practical applications across various domains are summarized, with extensions of IML to multiple labels as well as unaligned views. To advance this field, we conclude that adapting multi -view learning for incomplete data, addressing complex and arbitrary missing scenarios, tackling high missing ratios, exploring regularization techniques, reducing noise impact, and integrating IML with other learning paradigms are valuable research directions in the future.

关键词： Incomplete multi-view learning Consistency assumption Complementarity assumption Low-rank assumption Missing scenarios

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Robust two-stage instance-level cost-sensitive learning method for class imbalance problem

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knowledge-BASED SYSTEMS 2024年 300卷

作者： Tang, Jingjing Li, Yan Hou, Zhaojie Fu, Saiji Tian, Yingjie Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Inst Big Data Chengdu 611130 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS Social Sci Lab Digital Econ Forecasts & Policy Sim MOE Beijing 100190 Peoples R China

The class imbalance problem is a pervasive challenge in various applications, prompting researchers to design specialized loss functions for instance-level cost-sensitive learning models. However, the performance deteriorates when datasets are contaminated by label noise, especially in scenarios with uneven noise distribution. Additionally, specific hard-to-classify samples can further impede performance. Addressing these issues establishing a robust instance-level cost-sensitive learning framework remains a formidable challenge. this paper, we propose a robust Two-Stage instance-level cost-sensitive learning method with the Bounded Quadratic Type Squared Error (BQTSE) loss function, termed TSBQT. The asymmetric and bounded nature the BQTSE loss function not only allows for distinct cost assignments to majority and minority instances, also enhances the robustness to uneven noise. Moreover, our two-stage learning strategy prioritizes hard classify instances by considering their classification difficulty. We solve TSBQT using the alternating direction method of multipliers and gradient descent algorithm. The generalization capability of TSBQT is analyzed the Rademacher complexity theory. Extensive experiments validate the effectiveness of TSBQT.

关键词： Class imbalance problem Cost-sensitive learning Two-stage learning strategy BQTSE loss function label noise

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Structure Aware Loss for Deep Unsupervised Industrial Image Registration

Structure Aware Loss for Deep Unsupervised Industrial Image ...

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Yong Shi Mengjin Lyu Zhiquan Qi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha Omaha USA School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Image registration is extensively utilized in industrial applications. However, specific challenges inherent to these environments—such as low texture, significant displacement, and poor lighting conditions—complicate the registration process. This paper introduces a structure-aware loss module tailored to the unique characteristics of industrial images. This module promotes the network’s focus on salient regions while emphasizing structural features. Additionally, it is designed as a plug-in component, facilitating integration with any loss function and homography estimation method. We develop an unsupervised registration network and create an industrial dataset to evaluate the module’s effectiveness. A series of experiments demonstrate that our structure-aware module significantly enhances registration performance.

关键词： Deep learning Image registration Conferences Lighting Estimation data mining

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Analysis on the Transmission of International Oil Price to China’s Price Index--Based on Granger Causality Network

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Procedia Computer Science 2022年 199卷 1276-1283页

作者： Yijing Wang Xiuzhenzi Wang Kun Guo School of Economics and Management University of Chinese Academy of Science Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

This paper uses complex network theory and Granger causality method to construct Granger Causality Network among price indices (PIGCN), specifically based on crude oil price and China’s domestic price index from a systematic perspective and provides a statistical physical method to analyze conduction fluctuations. This paper selects four typical price indices in China, including 36 sub price indices as samples for empirical research. Through the analysis of the PIGCN structure, we have analyzed the volatility transmission path of the international crude oil price and Chinese domestic price index, and further revealed the dynamic relationship among the price indices. Also, we have put forward some policy recommendations to better monitor the spread of price fluctuation risks.

关键词： Granger causality network Price transmission International crude oil price Price indices

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Climate change unequally affects nitrogen use and losses in global croplands

NATURE FOOD

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NATURE FOOD 2023年第4期4卷 294-+页

作者： Ren, Chenchen Zhang, Xiuming Reis, Stefan Wang, Sitong Jin, Jiaxin Xu, Jianming Gu, Baojing Zhejiang Univ Coll Environm & Resource Sci Hangzhou Peoples R China Zhejiang Univ Dept Land Management Hangzhou Peoples R China Zhejiang Univ Policy Simulat Lab Hangzhou Peoples R China Univ Melbourne Sch Agr & Food Melbourne Vic Australia UK Ctr Ecol & Hydrol Penicuik Scotland Univ Exeter Med Sch Knowledge Spa Truro England Univ Edinburgh Sch Chem Edinburgh Scotland Hohai Univ Coll Hydrol & Water Resources Nanjing Peoples R China Hohai Univ Key Lab Water Big Data Technol Minist Water Resour Nanjing Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Agr Resources & Environm Hangzhou Peoples R China Zhejiang Univ Minist Educ Key Lab Environm Remediat & Ecol Hlth Hangzhou Peoples R China

Quantification of the impact of climate change on crop yield and nitrogen use efficiency reflects global inequalities in agricultural N use and losses. Using long-term historical data from over 150 countries, this study reveals the importance of farm size to regulate N use for climate adaptation. Maintaining food production while reducing agricultural nitrogen pollution is a grand challenge under global climate change. Yet, the response of global agricultural nitrogen uses and losses to climate change on the temporal and spatial scales has not been fully characterized. Here, using historical data for 1961-2018 from over 150 countries, we show that global warming leads to small temporal but substantial spatial impacts on cropland nitrogen use and losses. Yield and nitrogen use efficiency increase in 29% and 56% of countries, respectively, whereas they reduce in the remaining countries compared with the situation without global warming in 2018. Precipitation and farm size changes would further intensify the spatial variations of nitrogen use and losses globally, but managing farm size could increase the global cropland nitrogen use efficiency to over 70% by 2100. Our results reveal the importance of reducing global inequalities of agricultural nitrogen use and losses to sustain global agriculture production and reduce agricultural pollution.

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Self-distillation and self-supervision for partial label learning

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PATTERN RECOGNITION 2024年 146卷

作者： Yu, Xiaotong Sun, Shiding Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Polic Beijing 100190 Peoples R China

As a main branch of weakly supervised learning paradigm, partial label learning (PLL) copes with the situation where each sample corresponds to ambiguous candidate labels containing the unknown true label. The primary difficulty of PLL lies in label ambiguities, most existing researches focus on individual instance knowledge while ignore the importance of cross-sample knowledge. To circumvent this difficulty, an innovative multi-task framework is proposed in this work to integrate self-supervision and self-distillation to tackle PLL problem. Specifically, in the self-distillation task, cross-sample knowledge in the same batch is utilized to refine ensembled soft targets to supervise the distillation operation without using multiple networks. The auxiliary self-supervised task of recognizing rotation transformations of images provides more supervisory signal for feature learning. Overall, training supervision is constructed not only from the input data itself but also from other instances within the same batch. Empirical results on benchmark datasets reveal that this method is effective in learning from partially labeled data.

关键词： knowledge distillation Self-supervised learning Partial label learning Machine learning

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Nonparametric data-driven learning algorithms for multilocation inventory systems

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OPERATIONS RESEARCH LETTERS 2024年 57卷

作者： Zhong, Zijun He, Zhou City Univ Hong Kong Dept Mkt Kowloon Hong Kong Peoples R China Univ Chinese Acad Sci Sch Econ & Management 3 Zhongguancun Nanyitiao Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy 3 Zhongguancun Nanyitiao Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China

We study a multilocation inventory system with unknown demand distribution using a nonparametric approach. The system consists of multiple distribution centers and customer locations, where products are shipped from the distribution centers to fulfill customer demands. We propose a novel algorithm, DMLI, for adaptive inventory management. Under specific conditions, we establish that the average expected T -period regret of DMLI converges to the optimal rate of O(1/ root T). (c) 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Multilocation inventory system Nonparametric learning Online convex optimization Regret

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Discovering the relationship between the number of film review topics and box office with NLP techniques 11

Discovering the relationship between the number of film revi...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Li, Bo Dai, Wei Liu, Shang Shi, Yong School of Finance and Economics Wuhan City Vocational College No. 10 Yezhihu West Road Hongshan District Wuhan430064 China PKU-WUHAN Institute for Artificial Intelligence No. 770 Gaoxin Avenue Hongshan District Wuhan430070 China School of Economics Shanghai University 99 Shangda Road BaoShan District Shanghai200444 China School of Economics and Management University of Chinese Academy of Sciences No. 3 Zhongguancun South Yitiao Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website ***, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work. © 2024 The Authors.

关键词： Natural language processing systems

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