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Uncertain regression analysis: an approach for imprecise observations

SOFT COMPUTING 2018年第17期22卷 5579-5582页

作者： Yao, Kai Liu, Baoding Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

Regression analysis is a method to estimate the relationships among the response variable and the explanatory variables. Assuming the observations of the response variable are imprecise and modeling the observed data via uncertain variables, this paper explores an approach of uncertain regression analysis to estimating the relationships among the variables with imprecisely observed samples. On the principle of least squares, an optimization problem is derived to calculate the unknown parameters in the regression model. In particular, this paper investigates uncertain linear regression model and gives an analytic representation of the unknown parameters.

关键词： Uncertain regression Uncertain linear regression Uncertain variable Uncertainty theory

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Movie Recommendation Algorithm Based on Sentiment Analysis and LDA 8

Movie Recommendation Algorithm Based on Sentiment Analysis a...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Yilin Zhang, Lingling Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Sentiment Analysis Topic Extraction Content-based Recommendation Movie Reviews

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Improving Session Search by Modeling Multi-Granularity Historical Query Change 22

Improving Session Search by Modeling Multi-Granularity Histo...

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15th ACM International Conference on Web Search and data mining (WSDM)

作者： Zuo, Xiaochen Dou, Zhicheng Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Key Lab Data Engn & Knowledge Engn MOE Beijing Peoples R China

ISBN: (纸本)9781450391320

In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.

关键词： Session Search Query Change Document Ranking

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A theoretic study of a distance-based regression model

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Science China Mathematics 2019年第5期62卷 979-998页

作者： Jialu Li Wei Zhang Sanguo Zhang Qizhai Li School of Mathematics and Statistics Beijing Institute of Technology Biostatistics and Bioinformatics Branch National Institute of Child Health and Human Development School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences LSC NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences

The distance-based regression model has many applications in analysis of multivariate response regression in various ?elds, such as ecology, genomics, genetics, human microbiomics, and neuroimaging. It yields a pseudo F test statistic that assesses the relation between the distance(dissimilarity) of the subjects and the predictors of interest. Despite its popularity in recent decades, the statistical properties of the pseudo F test statistic have not been revealed to our knowledge. This study derives the asymptotic properties of the pseudo F test statistic using spectral decomposition under the matrix normal assumption, when the utilized dissimilarity measure is the Euclidean or Mahalanobis distance. The pseudo F test statistic with the Euclidean distance has the same distribution as the quotient of two Chi-squared-type mixtures. The denominator and numerator of the quotient are approximated using a random variable of the form ξχ_d^2+ η, and the approximate error bound is given. The pseudo F test statistic with the Mahalanobis distance follows an F *** simulation studies, the approximated distribution well matched the "exact" distribution obtained by the permutation procedure. The obtained distribution was further validated on H1N1 in?uenza data, aging human brain data, and embryonic imprint data.

关键词： distance-based regression Euclidean pseudo F test statistic Mahalanobis

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Solving uncapacitated P-Median problem with reinforcement learning assisted by graph attention networks

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APPLIED INTELLIGENCE 2023年第2期53卷 2010-2025页

作者： Wang, Chenguang Han, Congying Guo, Tiande Ding, Man Univ Chinese Acad Sci Sch Math Sci 19A Yuquan Rd Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhonguancun East Rd Beijing 100190 Peoples R China

The P-Median Problem is one of the basic cases of facility location problems and has been studied for many years. Most methods of solving it are based on classical heuristics or meta-heuristic and they don't perform well on large-scale problems according to time cost. In this paper, we propose the first reinforcement learning-based method which uses Multi-Talking-Heads Graph Attention Networks to learn representations and design a learnable attention mechanism to solve the uncapacitated P-Median Problem. We train the model using REINFORCE algorithm and show that it has good performance on uncapacitated P-Median Problem according to solution quality and time consumption. We also apply our model to the realistic dataset and empirically figure out that the difference between data distributions is one of the most important factors to influence the final performances.

关键词： P-median problem Graph neural networks Reinforcement learning

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Antenna Array Signal Direction of Arrival Estimation on Digital Signal Processor (DSP) 3

Antenna Array Signal Direction of Arrival Estimation on Digi...

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3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Ying Cui, Hongyuan Univ Chinese Acad Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Antenna arrays are used in many digital signal processing applications due to their ability to locate signal sources. Direction of Arrival (DOA) estimation is a key task of array signal processing. Although various algorithms have been developed for DOA estimation, their high complexity prevents their use in real-time applications. In this paper, we design and develop an efficient parallel implementation of DOA on DSP which is the most widely used processor in embedded system. Due to the potential parallelism in MUSIC algorithm, it is selected for 2-D DOA estimation. Two computational cores in MUSIC are identified and parallelized. Vectorization of multiple single precision floating point operations is proposed to make good use of the 128-bit vectors on DSP C6678. Then, the parallel DOA estimation algorithm is implemented on one core of DSP C6678 which is the latest version up to now. Experiments are conducted on both 1-D and 2-D antenna array signals. Considerable performance improvement is obtained. (C) 2015 Published by Elsevier B.V.

关键词： parallel computing signal processing DSP DOA estimation

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A Category-aware Multi-interest Model for Personalized Product Search 22

A Category-aware Multi-interest Model for Personalized Produ...

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31st ACM Web Conference (WWW)

作者： Liu, Jiongnan Dou, Zhicheng Zhu, Qiannan Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450390965

Product search has been an important way for people to find products on online shopping platforms. Existing approaches in personalized product search mainly embed user preferences into one single vector. However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user's multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. Specifically, we also capture the category indications for each preference to indicate the distribution of categories it focuses on, which is derived from rich relations between users, products, and attributes. Based on these category indications, we develop a category attention mechanism to aggregate these various preference embeddings considering current queries and items as the user's comprehensive representation. By this means, we can use this representation to calculate matching scores of retrieved items to determine whether they meet the user's search intent. Besides, we introduce a homogenization regularization term to avoid the redundancy between user interests. Experimental results show that the proposed method significantly outperforms existing approaches.

关键词： personalized product search multi-interest knowledge graph

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Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1051-1062页

作者： Li, Kai Wang, Bo Tian, Yingjie Qi, Zhiquan Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this article, we propose a pixel-based adaptive weighted cross-entropy (WCE) loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, that is, CrackForest, AigleRN, Crack360, and BJN260. Compared to the vanilla WCE, the proposed loss significantly speeds up the training process while retaining the performance.

关键词： Roads Training Costs Image edge detection Training data Sampling methods Adaptation models Crack detection Jaccard distance U-Net weighted cross-entropy (WCE)

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Matrix Sketching Over Sliding Windows 16

Matrix Sketching Over Sliding Windows

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ACM SIGMOD International Conference on management of data

作者： Wei, Zhewei Liu, Xuancheng Li, Feifei Shang, Shuo Du, Xiaoyong Wen, Ji-Rong Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn MOEBeijing Key Lab Big Data Management & Anal Me Beijing Peoples R China Univ Utah Sch Comp Salt Lake City UT 84112 USA

ISBN: (纸本)9781450335317

Large-scale matrix computation becomes essential for many data data applications, and hence the problem of sketching matrix with small space and high precision has received extensive study for the past few years. This problem is often considered in the row-update streaming model, where the data set is a matrix A E R'd, and the processor receives a row (1 x d) of A at each timestamp. The goal is to maintain a smaller matrix (termed approximation matrix, or simply approximation) B E Rex d as an approximation to A, such that the covariance error MATA BT B11 is small and < n. This paper studies continuous tracking approximations to the matrix defined by a sliding window of most recent rows. We consider both sequence-based and time-based window. We show that maintaining ATA exactly requires linear space in the sliding window model, as opposed to 0(d2) space in the streaming model. With this observation, we present three general frameworks for matrix sketching on sliding windows. The sampling techniques give random samples of the rows in the window according to their squared norms. The Logarithmic Method converts a mergeable streaming matrix sketch into a matrix sketch on time-based sliding windows. The Dyadic Interval framework converts arbitrary streaming matrix sketch into a matrix sketch on sequence based sliding windows. In addition to proving all algorithmic properties theoretically, we also conduct extensive empirical study with real data sets to demonstrate the efficiency of these algorithms.

关键词： matrix sketching sliding window

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On inexact stochastic splitting methods for a class of nonconvex composite optimization problems with relative error

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OPTIMIZATION METHODS & SOFTWARE 2023年第1期38卷 1-33页

作者： Hu, Jia Han, Congying Guo, Tiande Zhao, Tong Univ Chinese Acad Sci Sch Math Sci 19A Yuquan Rd Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China

We consider minimizing a class of nonconvex composite stochastic optimization problems, and deterministic optimization problems whose objective function consists of an expectation function (or an average of finitely many smooth functions) and a weakly convex but potentially nonsmooth function. And in this paper, we focus on the theoretical properties of two types of stochastic splitting methods for solving these nonconvex optimization problems: stochastic alternating direction method of multipliers and stochastic proximal gradient descent. In particular, several inexact versions of these two types of splitting methods are studied. At each iteration, the proposed schemes inexactly solve their subproblems by using relative error criteria instead of exogenous and diminishing error rules, which allows our approaches to handle some complex regularized problems in statistics and machine learning. Under mild conditions, we obtain the convergence of the schemes and their computational complexity related to the evaluations on the component gradient of the smooth function, and find that some conclusions of their exact counterparts can be recovered.

关键词： Nonsmooth nonconvex optimization stochastic approximation relative error alternating direction method of multipliers proximal gradient descent variance reduction

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