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SPATIAL REGULARIZATION OF canonical correlation analysis FOR LOW-RESOLUTION FACE RECOGNITION

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Transactions of Nanjing University of Aeronautics and Astronautics 2013年第1期30卷 77-81页

作者：周旭东陈晓红钱强 College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Information Engineering College Yangzhou University College of Science Nanjing University of Aeronautics and Astronautics

canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), *** and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially *** , for a multi-resolution scenario , there are no related *** , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution *** , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.

关键词： face recognition canonical correlation analysis low-resolution spatial information

Study on soil water characteristics of tobacco fields based on canonical correlation analysis

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Water Science and Engineering 2009年第2期2卷 79-86页

作者： Xiao-hou SHAO Yu WANG Li-dong BI You-bo YUAN Xian-kun SU Jian-guo MO College of Agricultural Engineering Hohai University Nanjing 210098 P. R. China Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil Water Environment in Southern China Ministry of Education Nanjing 210098 P. R. China Guizhou Tobacco Science Research Institute Guiyang 550003 P. R. China Guizhou Institute of Mountainous Environment and Climate Guiyang 550002 R R. China

In order to identify the principal factors influencing soil water characteristics （SWC） and evaluate SWC effectively, the multivariate-statistical canonical correlation analysis （CCA） method was used to study and analyze the correlation between SWC and soil physical and chemical properties. Twenty-two soil samples were taken from 11 main tobacco-growing areas in Guizhou Province in China and the soil water characteristic curves （SWCC） and basic physical and chemical properties of the soil samples were determined. The results show that：（1） The soil bulk density, soil total porosity and soil capillary porosity have significant effects on SWC of tobacco fiels. Bulk density and total porosity are positively correlated with soil water retention characteristics （SWRC）, and soil capillary porosity is positively correlated with soil water supply characteristics （SWSC）. （2） Soil samples from different soil layers at the same soil sampling point show similarity or consistency in SWC. Inadequate soil water supply capability and imbalance between SWRC and SWSC are problems of tobacco soil. （3） The SWC of loamy clay are generally superior to those of silty clay loam.

关键词： canonical correlation analysis tobacco soils soil water characteristics soil texture

Multi-view dimensionality reduction via canonical random correlation analysis

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Frontiers of Computer Science 2016年第5期10卷 856-869页

作者： Yanyan ZHANG Jianchun ZHANG Zhisong PAN Daoqiang ZHANG College of Command Information Systems PLA University of Science and Technology Nanjing 210007 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

canonical correlation analysis （CCA） is one of the most well-known methods to extract features from multi- view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross- view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis （RCA）. In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA （KRCA） is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.

关键词： canonical correlation analysis discriminant,multi-view dimensionality reduction

Fault Detection Based on canonical correlation analysis with Rank Constrained Optimization

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Fault Detection Based on Canonical Correlation Analysis with...

A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

第40届中国控制会议

作者： Yuhan Zhang Xianchao Xiu Ying Yang Wanquan Liu State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of EngineeringPeking University School of Intelligent Systems Engineering Sun Yat-sen University

canonical correlation analysis（CCA） has attracted increasing attention in the field of fault detection because it provides an effective way to explore the relationship between the input and output *** paper develops a novel rank constrained CCA（RCCCA） framework,which is the first approach that takes the rank prior information into ***,the rank constrained optimization is able to capture the global structures of variables,and thus improve the performance of fault *** order to solve RCCCA,an alternating minimization algorithm is designed,which aims to preserve the maximum correlation with the low-rank learning.A fault detection residual is then generated,and the test statistic is constructed to determine whether a fault *** RCCCA-based fault detection is finally tested on a numerical example and the Tennessee Eastman benchmark *** results indicate the efficiency and feasibility of the proposed method.

关键词： Fault detection canonical correlation analysis rank constrained optimization alternating minimization algorithm Tennessee Eastman process

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Computers, Materials & Continua 2024年第7期80卷 235-261页

作者： Jian Luo Bo Xu Tardi Tjahjadi Jian Yi College of Information Science and Engineering Hunan Normal UniversityChangsha410000China School of Engineering University of WarwickCoventryCV47ALUK

Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life *** paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body *** 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and *** 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition ***,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing *** degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and ***,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and ***,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other ***,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the *** research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.

关键词： Gait recognition human identification three-dimensional gait canonical correlation analysis

Multi-scale causal connectivity analysis by canonical correlation

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Multi-scale causal connectivity analysis by canonical correl...

canonical corelation analysis to land-use structure and its driving forces——Taking Yulin Prefecture as an example

2011中华医学会神经外科学学术会议

作者： CHEN FUYONG KANG DEZHI WU GUORONG CHEN HUAFU YU LIANGHONG LIN ZHANGYA LIN YUANXIANG 福建医科大学附属第一医院神经外科成都电子科技大学

Multivariate Granger causality is a potentially promising approach for inferring information flow based upon experimental data in complex systems, and mapping brain connectivity. Traditional Granger causality is based... 详细信息

关键词： ultivariate Granger causality canonical correlation analysis depth-EEG

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Journal of Geographical Sciences 1998年第2期8卷 73-79页

作者： ZHANG MingInstitute of Geography, CAS, Beijing 100101 CHINA

In this paper, one of the most classical statistical methods, canonical correlation analysis (CCA) is applied to identify quantitatively the driving forces of landuse structure in Yulin Prefecture. The main analysis is carried out through the software SPSS with the data on the level of towns and townships in 1992. The results indicate that landuse structure is determined by comprehensive action of different factors. Landuse structure with rural characteristics is mainly determined by geographical factors such as the elevation, temperature and precipitation, while the landuse structure with urban characteristics is mainly determined by demographic and socioeconomic conditions. At the same time, tests were carried out through the canonical correlation coefficient and redundancy analysis.

关键词： canonical correlation analysis Redundancy analysis landuse and landcover change driving force

Data-driven multimodal fusion:approaches and applications in psychiatric research

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Psychoradiology 2023年第1期3卷 135-153页

作者： Jing Sui Dongmei Zhi Vince D Calhoun State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Tni-institutional Center for Translational Research in Neuroimaging and Data Science(TReNDS) Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA 30303United States

In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion *** methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric ***,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of *** is where data-driven multi-modal fusion techniques come into *** combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain *** this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component *** applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain *** summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical ***,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

关键词： multimodal fusion approach data driven functional magnetic resonance imaging(fMRI) structural MRI diffusion mag-netic resonance imaging independent component analysis canonical correlation analysis psychiatric disorder

PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance

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International Journal of Automation and computing 2017年第2期14卷 136-146页

作者： Hong-Kai Chen Xiao-Guang Zhao Shi-Ying Sun Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.

关键词： Low-resolution human detection partial least squares canonical correlation analysis heterogeneous features outdoorvideo surveillance.