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Facial Expression Recognition of Various Internal States via manifold learning

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Journal of Computer Science & Technology 2009年第4期24卷 745-752页

作者： Young-Suk Shin IEEE Department of Information and Communication Engineering Chosun University

Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, facial expressions include mixed emotions, continuous rather than discrete, which vary from moment to moment. This paper represents a novel method of recognizing facial expressions of various internal states via manifold learning, to achieve the aim of humancentered interaction studies. A critical review of widely used emotion models is described, then, facial expression features of various internal states via the locally linear embedding （LLE） are extracted. The recognition of facial expressions is created with the pleasure-displeasure and arousal-sleep dimensions in a two-dimensional model of emotion. The recognition result of various internal state expressions that mapped to the embedding space via the LLE algorithm can effectively represent the structural nature of the two-dimensional model of emotion. Therefore our research has established that the relationship between facial expressions of various internal states can be elaborated in the two-dimensional model of emotion, via the locally linear embedding algorithm.

关键词： manifold learning locally linear embedding dimension model pleasure-displeasure dimension arousal-sleep dimension

Physical layer authentication in UAV-enabled relay networks based on manifold learning

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Science China(Information Sciences) 2022年第12期65卷 263-283页

作者： Shida XIA Xiaofeng TAO Na LI Shiji WANG Jin XU National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing University of Posts and Telecommunications Research Institute

An unmanned aerial vehicle(UAV) relay network is a promising solution in the next-generation wireless networks due to its high capacity and unlimited geography. However, because of the openness of wireless channels and UAV mobility, it is remarkably challenging to guarantee the secure access of UAV relay. In this paper, we investigate the physical layer authentication(PLA) to verify the identity of the UAV relay for preventing unauthorized access to users’ information or network service. Unlike most existing PLA methods for UAV, the proposed PLA scheme fully considers the time-varying of physical layer attributes caused by UAV mobility, and transforms the authentication problem into recognizing nonlinearly separable physical layer data. Particularly, we propose a manifold learning-based PLA scheme that can authenticate the mobile UAV relay in real time by establishing the local correlation of physical layer attributes. The Markov chain of physical layer data in the time domain is established to evaluate UAV state transition probability through the proposed diffusion map algorithm. The legitimate UAV and spoofing attackers can always be authenticated by the different motion states. Performance analysis offered a comprehensive understanding of the proposed scheme. Extensive simulations confirm that the performance of the proposed scheme improves over 18% in resisting the intelligent spoofing UAV compared with the traditional methods.

关键词： UAV relay physical layer authentication mobility manifold learning diffusion map state transition probability

Incremental Alignment manifold learning

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Journal of Computer Science & Technology 2011年第1期26卷 153-165页

作者：韩志孟德宇徐宗本古楠楠 Institute for Information and System Sciences Xi'an Jiaotong University

A new manifold learning method, called incremental alignment method （IAM）, is proposed for nonlinear dimensionality reduction of high dimensional data with intrinsic low dimensionality. The main idea is to incrementally align low-dimensional coordinates of input data patch-by-patch to iteratively generate the representation of the entire ***. The method consists of two major steps, the incremental step and the alignment step. The incremental step incrementally searches neighborhood patch to be aligned in the next step, and the alignment step iteratively aligns the low-dimensional coordinates of the neighborhood patch searched to generate the embeddings of the entire dataset. Compared with the existing manifold learning methods, the proposed method dominates in several aspects： high efficiency, easy out-of-sample extension, well metric-preserving, and averting of the local minima issue. All these properties are supported by a series of experiments performed on the synthetic and real-life datasets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically argued and experimentally demonstrated.

关键词： alignment incremental learning manifold learning nonlinear dimensionality reduction out-of-sample issue

Applying Ricci flow to high dimensional manifold learning

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Science China(Information Sciences) 2019年第9期62卷 23-36页

作者： Yangyang LI Ruqian LU Key Lab of Management Decision and Information Systems Academy of Mathematics and Systems ScienceChinese Academy of Sciences University of Chinese Academy of Sciences

In machine learning, a high dimensional data set such as the digital image of a human face is often viewed as a point set distributed on a differentiable manifold. In many cases, the intrinsic dimension of this manifold is low but the representation dimension of the data points is high. To ease data processing requirements, manifold learning(ML) techniques can be used to reduce a high dimensional manifold(HDM)to a low dimensional one while keeping the essential geometric properties, such as relative distances between points, unchanged. Traditional ML algorithms often assume that the local neighborhood of any point on an HDM is roughly equal to the tangent space at that point. This assumption leads to the disadvantage that the neighborhoods of points on the manifold, though they have a very different curvature, will be treated equally and will be projected to a lower dimensional space. The curvature is a different way of manifold processing, where traditional dimension reduction is ineffective at preserving the *** overcome this obstacle, we perform an "operation" on the HDM using Ricci flow before a manifold’s dimension reduction. More precisely, with the Ricci flow, we transform each local neighborhood of the HDM to a constant curvature patch. The HDM, as a whole, is then transformed into a subset of a sphere with constant positive curvature. We compare the proposed algorithm with other traditional manifold learning algorithms. Experimental results have shown that the proposed method outperforms other ML algorithms with a better neighborhood preserving rate.

关键词： manifold learning Ricci flow Ricci curvature dimension reduction curvature estimation

Indoor localization via l^1-graph regularized semi-supervised manifold learning

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The Journal of China Universities of Posts and Telecommunications 2012年第5期19卷 39-44,91页

作者： ZHU Yu-jia DENG Zhong-liang JI Hao School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

In this paper, a new -graph regularized semi-supervised manifold learning （LRSML） method is proposed for indoor localization. Due to noise corruption and non-linearity of received signal strength （RSS）, traditional approaches always fail to deliver accurate positioning results. The -graph is constructed by sparse representation of each sample with respect to remaining samples. Noise factor is considered in the construction process of -graph, leading to more robustness compared to traditional k-nearest-neighbor graph （KNN-graph）. The KNN-graph construction is supervised, while the -graph is assumed to be unsupervised without harnessing any data label information and uncovers the underlying sparse relationship of each data. Combining KNN-graph and -graph, both labeled and unlabeled information are utilized, so the LRSML method has the potential to convey more discriminative information compared to conventional methods. To overcome the non-linearity of RSS, kernel-based manifold learning method （K-LRSML） is employed through mapping the original signal data to a higher dimension Hilbert space. The efficiency and superiority of LRSML over current state of art methods are verified with extensive experiments on real data.

关键词： -graph indoor positioning semi-supervised manifold learning wireless local area network (WLAN)

Autonomic failure prediction based on manifold learning for large-scale distributed systems

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The Journal of China Universities of Posts and Telecommunications 2010年第4期17卷 116-124页

作者： LU Xu WANG Hui-qiang ZHOU Ren-jie GE Bao-yu College of Computer Science and Technology Harbin Engineering University Harbin 150001 China

This article investigates autonomic failure prediction in large-scale distributed systems with nonlinear dimensionality reduction to automatically extract failure features. Most existing methods for failure prediction focus on building prediction models or heuristic rules by discovering failure patterns, but the process of feature extraction before failure patterns recognition is rarely considered due to the increasing complexity of modern distributed systems. In this work, a novel performance-centric approach to automate failure prediction is proposed based on manifold learning （ML）. In addition, the ML algorithm named supervised locally linear embedding （SLLE） is applied to achieve feature extraction. To generalize the dimensionality reduction mapping, the nonlinear mapping approximation and optimization solution is also proposed. In experimental work a file transfer test bed with fault injection is developed which can gather multilevel performance metrics transparently. Based on the runtime monitoring of these metrics, the SLLE method can automatically predict more than 50% of the central processing unit （CPU） and memory failures, and around 70% of the network failure.

关键词： failure prediction manifold learning locally linear embedding autonomic computing

Atlas Compatibility Transformation:A Normal manifold learning Algorithm

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International Journal of Automation and computing 2015年第4期12卷 382-392页

作者： Zhong-Hua Hao Shi-Wei Ma Fan Zhao School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Power Station Automation TechnologyShanghai University

Over the past few years,nonlinear manifold learning has been widely exploited in data analysis and machine *** paper presents a novel manifold learning algorithm,named atlas compatibility transformation(ACT),It solves two problems which correspond to two key points in the manifold definition:how to chart a given manifold and how to align the patches to a global coordinate space based on *** the first problem,we divide the manifold into maximal linear patch(MLP) based on normal vector field of the *** the second problem,we align patches into an optimal global system by solving a generalized eigenvalue *** with the traditional method,the ACT could deal with noise datasets and fragment ***,the mappings between high dimensional space and low dimensional space are *** on both synthetic data and real-world data indicate the effection of the proposed algorithm.

关键词： Nonlinear dimensionality reduction manifold learning normal vector field maximal linear patch ambient space.

Research of Adaptive Neighborhood Incremental Principal Component Analysis and Locality Preserving Projection manifold learning Algorithm

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Journal of Shanghai Jiaotong university(Science) 2018年第2期23卷 269-275页

作者： DENG Shijie TANG Liwei ZHANG Xiaotao Department of Artillery Engineering Army Engineering University Shijiazhuang 050003 China

In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis(PCA) and locality preserving projection(LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.

关键词： incremental learning adaptive manifold learning fault diagnosis

Research on fault diagnosis method of piston rod based on harmonic wavelet and manifold learning

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High Technology Letters 2018年第3期24卷 232-240页

作者：江志农 Zhu Lina Zhang Jinjie Zhou Chao Diagnosis and Self-recovering Engineering Research Center Beijing University of Chemical TechnologyBeijing 1013029P.R.China Compressor Health and Intelligent Monitoring Center of National Key Laboratory of Compressor Technology Beijing University of Chemical TechnologyBeijing 100029P.R.China

As the core part of reciprocating compressor,piston rod is easy to cause a serious accident when abrasion and breakage fault occur to it. Therefore,it is very important to monitor its running state. At present,a small number of reciprocating compressors have been installed on-line monitoring and diagnosis system,most of which can only monitor a single vertical subsidence of piston rod and it can't fully represent the running state of piston rod. Therefore,a method of monitoring the vertical and horizontal displacement of piston rod axis orbit is simultaneously used. In view of the characteristics that the piston rod axis orbit is disordered and difficult to extract features,purification of the axis orbit is carried out based on harmonic wavelet and then features are extracted such as vibration energy,natural frequency and the axis orbit envelope area. After that,a nonlinear local tangent space manifold learning algorithm is used to reduce the dimension of the features and obtain sensitive features. By analyzing the practical cases,the effectiveness of the method for fault monitoring and diagnosis of reciprocating compressor piston rod assembly has been verified. Finally,as BP neural network has the characteristics of solving complex nonlinear problems,the validity of the fault diagnosis method of reciprocating compressor piston rod based on harmonic wavelet and manifold learning is proved by actual case data analysis based on BP neural network.

关键词： piston rod reciprocating compressor axis orbit harmonic wavelet manifold learning