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7th International Conference on Information Technology and Quantitative management (ITQM) - Information Technology and Quantitative management Based on Artificial Intelligence

作者： Niu, Yuting Shang, Yuan Tian, Yingjie Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Smart City Inst Zhengzhou 450001 Peoples R China Zhengzhou Univ Supercomp Ctr Smart City Inst Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l(1) norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： multi-view learning classification feature selection sparsity regularization linear programming

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Analyzing Health Seeking Behavior of Chinese Residents and Their Influencing Factors Based on CHNS data 7

Analyzing Health Seeking Behavior of Chinese Residents and T...

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7th International Conference on Information Technology and Quantitative management (ITQM) - Information Technology and Quantitative management Based on Artificial Intelligence

作者： Xue, Jiayu Ren, Xinyue Xu, Yuanjie Feng, Qianqian Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Chinese Acad Sci Sino Danish Coll Beijing Peoples R China Chinese Acad Sci Inst Sci & Dev Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

With the improvement of people's living standards, Chinese residents have paid more and more attention to health. The development of science and technology has made medical health big data emerge and has gradually become a research hotspot. In our paper, we first adopted Logistic Regression to find out the important variables for health seeking behavior and then we verified that this behavior can be predicted based on machine learning algorithms. By comparing classic Support Vector Machine (SVM) algorithm and improved SVM model with different variables, we finally proposed a SVM model based on SMOTE algorithm that is relatively optimal for predicting health seeking behavior. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Health seeking behavior Logistic Regression Support Vector Machine SMOTE

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Deep learning compact binary codes for fingerprint indexing

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Frontiers of Information Technology & Electronic Engineering 2018年第9期19卷 1112-1123页

作者： Chao-chao BAI Wei-qiang WANG Tong ZHAO Ru-xin WANG Ming-qiang LI School of Computer and Control University of Chinese Academy of Science School of Mathematical Sciences University of Chinese Academy of Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science

With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features,but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code（DCBMCC）as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code（MCC）, as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are ***, we propose a novel fingerprint indexing method based on deep neural networks to learn *** novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning ***, a multi-index hashing（MIH） based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.

关键词： Fingerprint indexing Minutia cylinder code Deep neural network Multi-index hashing

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Split Multiplicative Multi-View Subspace Clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2019年第10期28卷 5147-5160页

作者： Yang, Zhiyong Xu, Qianqian Zhang, Weigang Cao, Xiaochun Huang, Qingming Chinese Acad Sci State Key Lab Informat Secur Inst Informat Engn Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Beijing 100049 Peoples R China Cyberspace Secur Res Ctr Peng Chong Lab Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 101408 Peoples R China

Various subspace clustering methods have been successively developed to process multi-view datasets. Mast of the existing methods try to obtain a consensus structure coefficient matrix based on view-specific subspace recoveries. However, since view-specific structures contain individualized components that are intrinsically different from the consensus structure, directly adopting view-specific subspace structures might not be a reasonable choice. In this paper, with this concern in mind, our goal is to seek novel strategies to extract valuable components from view-specific structures that are consistent with the consensus subspace structure. To this end, we propose a novel multi-view subspace clustering method named split multiplicative multi-view subspace clustering ((SMSC)-S-2) with the joint strength of a multiplicative decomposition scheme and a variable splitting scheme. Specifically, the multiplicative decomposition scheme effectively guarantees the structural consistency of the extracted components. Then, the variable splitting scheme takes a step further via extracting the structural consistent components from view-specific structures. Furthermore, an alternating optimization algorithm is proposed to optimize the resulting optimization problem, which is non-convex and constrained. We prove that this algorithm could converge to a critical point. Finally, we provide empirical studies on real-world datasets that speak to the practical efficacy of our proposed method. The source code is released on GitHub https://***/joshuaas/SM2SC.

关键词： Computational and artificial intelligence artificial intelligence learning systems unsupervised learning and computers and information processing image processing image representation

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A divide-and-conquer method for large scale ν-nonparallel support vector machines

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NEURAL COMPUTING & APPLICATIONS 2018年第9期29卷 497-509页

作者： Ju, Xuchan Tian, Yingjie Tsinghua Univ Sch Econ & Management Beijing 100084 Peoples R China Bank China Postdoctoral Programme Agr Beijing 100005 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

Recently, nonparallel support vector machine (NPSVM), a branch of support vector machines (SVMs), is developed and has attracted considerable interest. A kind of developed NPSVM, nu-nonparallel support vector machine (nu-NPSVM), which inherits the advantages of nu-support vector machine (nu-SVM) enables us to achieve higher classification accuracy and less time to tune for parameters. However, applications of nu-NPSVM to large data sets are seriously hampered by their excessive training time. In this paper, we use divide-and-conquer technique for large scale nu-nonparallel support vector machine (DC-nu NPSVM) aiming at overcoming this burden. In the division step, we first divide the whole samples into several smaller subsamples and solve smaller subproblems using nu-NPSVM independently. We theoretically and experimentally show that objective function value, solutions, and support vectors solved by (DC-nu NPSVM) are likely to those of the whole nu-NPSVM. In the conquer step, subsolutions from subproblems are used as an initial coordinate descent solver which converges to the optimal solution quickly. Moreover, multi-level (DC-nu NPSVM) is adopted to balance the accuracy and efficiency. (DC-nu NPSVM) can achieve higher accuracy by tuning the parameters in a smaller range and control the number of support vectors efficiently. Quantities of experiments show our (DC-nu NPSVM) outperforms state-of-the-art SVM methods for large scale data sets.

关键词： Support vector machines nu-Nonparallel support vector machines Large scale data sets Divide-and-conquer

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Stacked Cross Refinement Network for Edge-Aware Salient Object Detection

Stacked Cross Refinement Network for Edge-Aware Salient Obje...

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International Conference on Computer Vision (ICCV)

作者： Zhe Wu Li Su Qingming Huang School of Computer Science and Technology University of Chinese Academy of Sciences (UCAS) Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS Beijing China Peng Cheng Laboratory ShenZhen China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Salient object detection is a fundamental computer vision task. The majority of existing algorithms focus on aggregating multi-level features of pre-trained convolutional neural networks. Moreover, some researchers attempt to utilize edge information for auxiliary training. However, existing edge-aware models design unidirectional frameworks which only use edge features to improve the segmentation features. Motivated by the logical interrelations between binary segmentation and edge maps, we propose a novel Stacked Cross Refinement Network (SCRN) for salient object detection in this paper. Our framework aims to simultaneously refine multi-level features of salient object detection and edge detection by stacking Cross Refinement Unit (CRU). According to the logical interrelations, the CRU designs two direction-specific integration operations, and bidirectionally passes messages between the two tasks. Incorporating the refined edge-preserving features with the typical U-Net, our model detects salient objects accurately. Extensive experiments conducted on six benchmark datasets demonstrate that our method outperforms existing state-of-the-art algorithms in both accuracy and efficiency. Besides, the attribute-based performance on the SOC dataset show that the proposed model ranks first in the majority of challenging scenes. Code can be found at https://***/wuzhe71/SCAN.

关键词： Image edge detection Task analysis Feature extraction Object detection Image segmentation Silicon Computational modeling

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DWWP: Domain-specific new words detection and word propagation system for sentiment analysis in the tourism domain

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knowledge-BASED SYSTEMS 2018年 146卷 203-214页

作者： Li, Wei Guo, Kun Shi, Yong Zhu, Luyao Zheng, Yuanchun 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 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Online travel has developed dramatically during the past three years in China. This results in a large amount of unstructured data like tourism reviews from which it is hard to extract useful knowledge. In this paper, a DWWP system consisting of domain-specific new words detection (DW) and word propagation (WP) is presented. DW deals with the negligence of user-invented new words and converted sentiment words by means of AMI (Assembled Mutual Information). Inspired by social networks, the new method WP incorporates manually calibrated sentiment scores, semantic and statistical similarity information, which improves the quality of sentiment lexicon in comparison with existing data-driven methods. Experimental results show that DWWP improves seventeen percentage points compared with graph propagation and four percentage points compared with label propagation in terms of accuracy on dataset I and dataset II, respectively. (C) 2018 Published by Elsevier B.V.

关键词： Online travel DWWP Chinese new words detection Sentiment lexicon Word propagation

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Personalizing Search Results Using Hierarchical RNN with Query-aware Attention 18

Personalizing Search Results Using Hierarchical RNN with Que...

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27th ACM International Conference on Information and knowledge management (CIKM)

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Univ Montreal DIRO Montreal PQ Canada Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Beijing Inst Technol Natl Engn Lab Big Data Syst Software Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450360142

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.

关键词： search results personalization hierarchical recurrent neural network query-aware attention

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High Performance Geological Disaster Recognition using Deep Learning

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Procedia Computer Science 2018年 139卷 529-536页

作者： Ying Liu Linzhi Wu University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Geological disaster recognition on optical image is one of the key techniques in disaster control and disaster relief. Comparing with optical images, remote sensing images contain much higher resolution and more visualized contents. In this paper, we propose a landslide recognition framework which trains a deep auto-encoder network on the compressed domain. ANN or SVM is used as the classifier for decision making. In addition, in order to meet the requirement of some real-time applications, a high performance training network on CUDA-enabled GPUs is designed and implemented. Experiments are conducted on optical images from Google Earth.

关键词： deep learning geological disaster recognition parallel computing CUDA

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Recognition and Classification of Rotorcraft by Micro-Doppler Signatures Using Deep Learning 18th

Recognition and Classification of Rotorcraft by Micro-Dopple...

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18th International Conference on Computational Science (ICCS)

作者： Liu, Ying Liu, Jinyi Univ Chinese Acad Sci Sch Comp & Control Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9783319936987;9783319936970

Detection and classification of rotorcraft targets are of great significance not only in civil fields but also in defense. However, up to now, it is still difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving targets by micro-Doppler features has become a promising research topic in the modern signal processing field. In this paper, we propose to use the deep convolutional neural networks (DCNNs) in rotorcraft detection and helicopter classification based on Doppler radar signals. We apply DCNN directly to raw micro-Doppler spectrograms for rotorcraft detection and classification. The proposed DCNNs can learn the features automatically from the micro-Doppler signals without introducing any domain background knowledge. Simulated data are used in the experiments. The experimental results show that the proposed DCNNs achieve superior accuracy in rotorcraft detection and superior accuracy in helicopter classification, outperforming the traditional radar signal processing methods.

关键词： Convolutional neural network Deep learning Target detection Classification Micro-Doppler

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