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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

Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing manifold learning

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Chinese Journal of Mechanical Engineering 2013年第5期26卷 1041-1049页

作者： CHEN Xiaoguang LIANG Lin XU Guanghua LIU Dan School of Mechanical Engineering Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University

The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

关键词： feature extraction manifold learning self-organize mapping kernel regression local tangent space alignment

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 learning.This 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 compatibility.For the first problem,we divide the manifold into maximal linear patch(MLP) based on normal vector field of the manifold.For the second problem,we align patches into an optimal global system by solving a generalized eigenvalue problem.Compared with the traditional method,the ACT could deal with noise datasets and fragment datasets.Moreover,the mappings between high dimensional space and low dimensional space are given.Experiments 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.

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 neighborhood.To 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

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 data.set. 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

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

An improved Isomap method for manifold learning

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International Journal of Intelligent Computing and Cybernetics 2017年第1期10卷 30-40页

作者： Taiguo Qu Zixing Cai College of Computer Science and Technology Hengyang Normal UniversityHengyangChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangChina School of Information Science and Engineering Central South UniversityChangshaChina

Purpose-Isometric feature mapping(Isomap)is a very popular manifold learning method and is widely used in dimensionality reduction and data visualization.The most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood graph.The classical Isomap(C-Isomap)is very slow,due to the use of Floyd’s algorithm to compute the shortest paths.The purpose of this paper is to speed up Isomap.Design/methodology/approach-Through theoretical analysis,it is found that the neighbourhood graph in Isomap is sparse.In this case,the Dijkstra’s algorithm with Fibonacci heap(Fib-Dij)is faster than Floyd’s algorithm.In this paper,an improved Isomap method based on Fib-Dij is proposed.By using Fib-Dij to replace Floyd’s algorithm,an improved Isomap method is presented in this paper.Findings-Using the S-curve,the Swiss-roll,the Frey face database,the mixed national institute of standards and technology database of handwritten digits and a face image database,the performance of the proposed method is compared with C-Isomap,showing the consistency with C-Isomap and marked improvements in terms of the high speed.Simulations also demonstrate that Fib-Dij reduces the computation time of the shortest paths from O(N3)to O(N2lgN).Research limitations/implications-Due to the limitations of the computer,the sizes of the data sets in this paper are all smaller than 3,000.Therefore,researchers are encouraged to test the proposed algorithm on larger data sets.Originality/value-The new method based on Fib-Dij can greatly improve the speed of Isomap.

关键词： Dijkstra’s algorithm Fibonacci heap Floyd’s algorithm Isomap manifold learning Shortest path Paper type Research paper

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：邓士杰唐力伟张晓涛 Department of Artillery Engineering Army Engineering University

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