Low-dimensional Manifold Space Research Articles

Learning to Locate: Adaptive Fingerprint-Based Localization With Few-Shot Relation Learning in Dynamic Indoor Environments

WiFi fingerprint-based localization has been intensive studies as a promising technology of ubiquitous location-based services. Two main concerns for its wide spread applications are to tackle with the cumbersome efforts of site survey and to combat vulnerable environment changes. To address these issues comprehensively, we propose a novel approach on adaptive fingerprint-based localization with less site survey, named as LESS, by exploring a new paradigm of radio map construction and adaptation with few-shot relation learning. Firstly, we extend sparsely collected fingerprints with the fingerprint augmentation method which produces new related data and derives their location information based on local proximity property in a low-dimensional manifold space. Then, LESS designs deep relation networks to learn not only the appropriate features but also a transferable deep-distance metric for modeling the fundamental relationships of the neighborhood fingerprints. Finally, once trained, LESS can quickly establish the neighborhood relationships among new fingerprints in the changed surroundings to realize adaptive location estimations, even without the network updating. The extensive experimental results demonstrate that LESS can achieve an attractive trade-off between the system overhead and the location performance with the superiorities over others in dynamic indoor environments.

Three-dimensional aerodynamic shape inverse design based on ISOMAP

Due to the numerous challenges associated with CFD calculations and high-dimensional data processing, it is challenging for the current approaches to satisfy the demands of inverse design of three-dimensional aerodynamic configuration. This work provides an effective inverse design method for three-dimensional aerodynamic configuration based on Isometric Feature Mapping (ISOMAP) in manifold learning. In this approach, the aerodynamic shape and parameterized control volume are regarded as the spatial nonlinear coupling surface, which is then projected into the low-dimensional manifold space via the ISOMAP approach to producing the manifold structure. The deformation control points' pressure distribution, which is paired with the samples to create a snapshot matrix, is interpolated from the resultant manifold structure. The Gappy POD (Proper Orthogonal Decomposition) approach is then employed to complete the missing design variables within the target pressure distribution of the deformation control points. The original aerodynamic shape is then parameterized using the FFD (Free Form Deform) method to create an aerodynamic shape that matches the target pressure distribution. The M6 wing and the RAE2822 wing are used as the initial models to verify the design method. The aerodynamic response error of the inverse design wing and the target wing is within 2%. The results demonstrate that the proposed three-dimensional aerodynamic shape inverse design approach has a high design accuracy, dramatically improves design efficiency, and significantly reduces the dimensionality required for three-dimensional aerodynamic shape inverse design.

A novel compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer

Metric transfer learning overcomes the problem of no domain distribution alienation in traditional sample transfer, and also improves the classification accuracy. However, some problems exist; for example, the target domain must have labelled samples without considering the manifold connection between data samples, and the algorithm is not robust under sparse samples. A compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer is proposed. First, the pseudo-label of the target domain is acquired through a weighted graph constructed on the source domain with known fault labels. In order to ensure the accuracy of metrics in manifold space, we adopt the method of graph label propagation (LP) to gradually spread the label information. Then, the sample data are mapped to the low-dimensional manifold space by locality preserving projections (LPP), which gives higher weight to the source domain samples close to the target domain to solve the domain offset problem. At the same time, combined with the labelled samples of the target domain in LP, the manifold distance measure is learned to minimize the intra-class distance and maximize the class spacing, eliminate the overlap phenomenon in the domain and realize the distribution alienation between classes in the domain. After each iteration, unsupervised manifold metric transfer learning is realized by increasing the range of LP and gradually reducing the error of graph LP. Experiments show that the new unsupervised transfer learning method has better fault identification ability than the semi-supervised transfer learning method. In the case of sparse training samples, it still maintains a high diagnostic accuracy, and the highest fault recognition accuracy can reach more than 97% with good robustness.

Research on Shaft Current Damage Identification of Variable Condition Motor Bearings Based on Multiscale Feature Label Propagation and Manifold Metric Transfer

The current damage is the most stubborn and difficult fault of high-power motor bearings because its vibration characteristics are easily confused with those of ordinary bearing mechanical faults. If it is discriminated as an ordinary mechanical fault without electrical insulation protection, the current damage of bearing shafts will still repeatedly appear. Aiming at the problem that it is difficult to identify the bearing current damage fault under variable working conditions, a bearing shaft current damage identification method based on multiscale feature label propagation and manifold metric transfer (MFLP-MMT) is proposed. Firstly, the multiscale sub-band signal is obtained by wavelet packet decomposition, and the multiscale sub-band fuzzy entropy is obtained by calculating its fuzzy entropy. Then, according to the extracted features, a neighbor graph is constructed on the source domain of the known fault label to obtain the pseudo label of the target domain sample, and the source domain label information is gradually diffused by way of the graph label propagation. The multiscale sub-band fuzzy entropy of the sample is mapped to the low-dimensional manifold space by locality preserving projections (LPP), and the source domain samples close to the target domain are given higher weights by cross-domain density ratio estimation to solve the problem of domain offset. Combined with the label samples of the target domain in label propagation, the manifold distance metric is learned to minimize the intra-class distance and maximize the inter-class distance in the domain and eliminate the overlapping phenomenon in the domain. By increasing the range of label propagation after each iteration, the label propagation error of the leading graph is gradually reduced, and unsupervised metric transfer learning is realized. The experimental results show that the new method is superior to the semi-supervised transfer learning method in fault identification ability; the highest fault identification accuracy can reach 100% and it has a good robustness.

Model reduction for the material point method via an implicit neural representation of the deformation map

This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the kinematics by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold. By explicitly approximating the deformation map, its spatiotemporal gradients—in particular the deformation gradient and the velocity—can be computed via analytical differentiation. In contrast to typical model-reduction techniques that construct a linear or nonlinear manifold to approximate the (finite number of) degrees of freedom characterizing a given spatial discretization, the use of an implicit neural representation enables the proposed method to approximate the continuous deformation map. This allows the kinematic approximation to remain agnostic to the discretization. Consequently, the technique supports dynamic discretizations—including resolution changes—during the course of the online reduced-order-model simulation.To generate dynamics for the generalized coordinates, we propose a family of projection techniques. At each time step, these techniques: (1) Calculate full-space kinematics at quadrature points, (2) Calculate the full-space dynamics for a subset of ‘sample’ material points, and (3) Calculate the reduced-space dynamics by projecting the updated full-space position and velocity onto the low-dimensional manifold and tangent space, respectively. We achieve significant computational speedup via hyper-reduction that ensures all three steps execute on only a small subset of the problem's spatial domain. Large-scale numerical examples with millions of material points illustrate the method's ability to gain an order of magnitude computational-cost saving—indeed real-time simulations—with negligible errors.

Non-negative multi-label feature selection with dynamic graph constraints

Feature selection can combat dimension disasters and improve the performance of classification algorithms, so multi-label feature selection is an essential part of multi-label learning and has attracted widespread attention. Many existing multi-label feature selection methods either do not consider the correlation between labels or directly use logical labels to guide the feature selection process, which leads to the loss of label information. This paper proposes a non-negative multi-label feature selection (NMDG) with dynamic graph constraints to address this issue. In the NMDG model, the original data space is projected into a low-dimensional manifold space by linear regression to construct the pseudo label matrix. The pseudo label matrix has the same topological structure as the original data by combining the non-negative constraints and the label graph matrix. Then, the robust low-dimensional space of the pseudo label matrix is used to construct the dynamic graph matrix, which is combined with the feature manifold to guide the learning of the feature weight matrix. Finally, we design an iterative algorithm based on alternating optimization to solve the proposed method and give convergence proof. Experimental results on ten real multi-label data sets compared with seven representative methods show the effectiveness of the proposed method.

Multi-label feature selection via manifold regularization and dependence maximization

Feature selection is able to select more discriminative features for classification and plays an important role in multi-label learning to alleviate the effect of the curse of dimensionality. Recently, the multi-label feature selection methods based on the sparse regression model have received increasing attentions. However, most of these methods directly project original data space to label space in the regression model, which is inappropriate because the linear assumption between data space and label space doesn't hold in most cases. In the paper, we propose a feature selection method named multi-label feature selection via manifold regularization and dependence maximization (MRDM). In the regression model of MRDM, the original data space is projected to a low-dimensional manifold space, which not only has the same topological structure with the original data, but also has a strong dependence with the class labels. Then, an objective function involving l2,1-norm regularization is formulated, and an alternating optimization-based iterative algorithm is designed to obtain the sparse coefficients for multi-label feature selection. Extensive experiments on various multi-label data sets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods.

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

Modified Label Propagation on Manifold With Applications to Fault Classification

In process monitoring, fault classification performance heavily relies on the labels of training data. However, the labeled data are inadequate and difficult to obtain because they require experienced human annotators. In this paper, a modified label propagation (MLP) method is proposed to propagate labels from labeled data to unlabeled data. The proposed label propagation algorithm has the following advantages: (1) It constructs a global and local consistency framework with the aid of a data graph, manifold learning, and data labels. This framework follows the assumption that data on the manifold will have similar structures, and nearby data will have similar labels. (2) Considering the inner relationship between the unlabeled data and historical data, a new definition for the initial label matrix is offered, which is significant for label propagation. (3) The new method propagates labels in a low-dimensional manifold space, which is different from most existing label propagation methods that propagate them in the original space. The results reveal that under the global and local consistency framework, soft labels of unlabeled data are given more effective predictions. With additional soft labels of unlabeled data, the MLP-based fault classification method is introduced. The simulation results obtained using a toy example demonstrate the label propagation performance of the MLP, and those obtained for the penicillin fermentation process verify the effectiveness of the MLP-based fault classification method.

Unsupervised Nonnegative Adaptive Feature Extraction for Data Representation

In this paper, we propose a novel unsupervised Nonnegative Adaptive Feature Extraction (NAFE) algorithm for data representation and classification. The formulation of NAFE integrates the sparsity constrained nonnegative matrix factorization (NMF), representation learning, and adaptive reconstruction weight learning into a unified model. Specifically, NAFE performs feature and weight learning over the new robust representations of NMF for more accurate measure and representation. For nonnegative adaptive feature extraction, our NAFE first utilizes the sparsity constrained NMF to obtain the new and robust representations of the original data. To preserve the manifold structures of the learnt new representations, we also incorporate a neighborhood reconstruction error over the weight matrix for joint minimization. Note that to further improve the representation power, the weights are jointly shared in the new low-dimensional nonnegative representation space, low-dimensional nonlinear manifold space, and low-dimensional projective subspace, i.e., local neighborhood information is clearly preserved in different feature spaces so that informative representations and features can be jointly obtained. To enable NAFE to extract features from new data, we also include a feature approximation error by a linear projection so that the learnt extractor can obtain features from new data efficiently. Extensive simulations show that our formulation can deliver state-of-the-art results on several public databases for feature extraction and classification, compared with several related methods.

Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains.

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.

Multi-view Locality Low-rank Embedding for Dimension Reduction

During the last decades, we have witnessed a surge of interest in learning a low-dimensional space with discriminative information from one single view. Even though most of them can achieve satisfactory performance in certain situations, they fail to fully consider the information from multiple views which are highly relevant but sometimes look different from each other. Besides, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view subspace learning methods. Therefore, how to learn an appropriate subspace which could maintain valuable information from multi-view features is of vital importance but challenging. To tackle this problem, this paper proposes a novel multi-view dimension reduction method named Multi-view Locality Low-rank Embedding for Dimension Reduction (MvL2E). MvL2E mainly focuses on capturing a common low-dimensional embedding among multiple different views, which makes full use of correlations between multi-view features by adopting low-rank representations. Meanwhile, it aims to maintain the correlations and construct a suitable manifold structure to capture the low-dimensional embedding for multi-view features. A centroid based scheme is designed to get one common low-dimensional manifold space and force multiple views to learn from each other. And an iterative alternating strategy is developed to obtain the optimal solution of MvL2E. The proposed method is evaluated on 5 benchmark datasets. Comprehensive experiments show that our proposed MvL2E can achieve comparable performance with previous approaches proposed in recent works of literature.

Patient handling activity recognition through pressure-map manifold learning using a footwear sensor

Abstract The risk of overexertion injury caused by patient handling and movement activities causes chronic pain and other physical and social impairments among the nursing force. The accurate recognition of patient handling activities (PHA) is the first step to reduce injury risk for caregivers. The current practice on workplace activity recognition is neither accurate nor convenient to perform. In this paper, we propose a novel solution comprising a smart footwear device and an action manifold learning framework to address the challenge. The wearable device, called Smart Insole, is equipped with a rich set of sensors and can provide an unobtrusive approach to obtain and characterize the action information of patient handling activities. Our proposed action manifold learning (AML) framework extracts the intrinsic signature structure by projecting raw pressure data from a high-dimensional input space to a low-dimensional manifold space. This framework not only performs dimension reduction but also reduces motion artifacts, which is robust against the noise and inter-class/intra-class variation in PHA recognition. To validate the effectiveness of the proposed framework, we perform a pilot study with eight subjects including eight common activities in a nursing room. The intrinsic dimensionality of the manifold is estimated by comparing the residual variances of different dimensionality settings. The experimental results show the overall classification accuracy achieves 86.6%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.9% and 88.3%, respectively.

Neighbourhood sensitive preserving embedding for pattern classification

Recently, a large family of supervised or unsupervised manifold learning algorithms that stem from statistical or geometrical theory has been designed to solve the problem of pattern classification. In this study, consider the fact that the data are usually sampled from a low-dimensional manifold space which resides in a high-dimensional Euclidean space, the authors propose a novel two-graph-based supervised linear classification algorithm called neighbourhood sensitive preserving embedding (NSPE). Different from local linear embedding (LLE) (or neighbourhood preserving embedding (NPE)) which preserves the local neighbourhood structure with one graph, NSPE can discover both the intrinsic and discriminant structure of the data manifold by constructing two graphs, that is, the within-class graph and the between-class graph. Thus, the data are mapped into a subspace where the nearby points with the same label are close to each other, whereas the nearby points with different labels are far apart. As a classification method, besides being defined on training samples, NSPE is also defined on testing samples. Experiments carried on the real-world face databases demonstrate that the results of all two-graph-based spectral methods are comparable and better than that of one-graph-based methods.

A sparse embedding and least variance encoding approach to hashing.

Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is, however, difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state-of-the-art methods.

Embedding Motion and Structure Features for Action Recognition

We propose a novel method to model human actions by explicitly coding motion and structure features that are separately extracted from video sequences. Firstly, the motion template (one feature map) is applied to encode the motion information and image planes (five feature maps) are extracted from the volume of differences of frames to capture the structure information. The Gaussian pyramid and center-surround operations are performed on each of the six obtained feature maps, decomposing each feature map into a set of subband maps. Biologically inspired features are then extracted by successively applying Gabor filtering and max pooling on each subband map. To make a compact representation, discriminative locality alignment is employed to embed the high-dimensional features into a low-dimensional manifold space. In contrast to sparse representations based on detected interest points, which suffer from the loss of structure information, the proposed model takes into account the motion and structure information simultaneously and integrates them in a unified framework; it therefore provides an informative and compact representation of human actions. The proposed method is evaluated on the KTH, the multiview IXMAS, and the challenging UCF sports datasets and outperforms state-of-the-art techniques on action recognition.

Improved <I>e</I> Dominance by Isomap

PDF HTML阅读 XML下载 导出引用 引用提醒 一种改进ε支配的等度规映射方法 DOI: 10.3724/SP.J.1001.2011.03933 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(60703107, 61001202); 国家高技术研究发展计划(863)(2009AA12Z210); 中国博士后科学基金 (20090451369); 新世纪优秀人才支持计划(NCET-08-0811); 中央高校基本科研业务费专项资金(JY10000902039); 北京市自然科学 基金(7092020); 陕西省自然科学基础研究计划(2009JQ8015); 陕西省“13115”科技创新工程重大科技专项(2008ZDKG-37) Improved ε Dominance by Isomap Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:新型支配机制的研究是进化多目标优化领域中的热点和难点之一,其中,ε支配最具代表性.但是,它的缺点在于对于不同几何形状的Pareto 前沿十分敏感.提出了一种改进ε支配机制的等度规映射方法,采用等度规映射把解映射到低维流形空间,发现隐藏于非支配解的几何分布,在低维流形空间进行ε支配的剪枝操作.与传统的ε支配相比,该机制不会丢失部分有效解,能够较好地保持解分布的均匀性.另外,为了克服传统ε支配丢失部分极端解的不足,设计了极端解校验算子.与NSGAII,SPEA2,NNIA 和εMOEA 相比,所提出的ε支配和极端解校验算子能够较好地保持解分布的均匀性和宽广性,改进了传统的ε支配. Abstract:The study of new types of dominance mechanisms is a key point in current evolutionary multiobjective optimization community and ε dominance is a representative among them. However, their ability in diversity maintaining is sensitive to different shapes of Pareto fronts. This paper proposes an improved ε dominance mechanism by Isomap, which employs Isomap to embed the original population to low dimensional manifold space. The intrinsic geometric structure of them is discovered and ε dominance is adopted to select data in the embedding space. Compared with traditional ε dominance, the mechanism does not lose valid solutions and can maintain a set of uniform-distributed solutions. In addition, the extreme solution-check operator is proposed to enhance the ability of holding extreme solutions of ε dominance. The detailed experimental comparison with NSGAII, SPEA2, NNIA and εMOEA shows that the two strategies in this study are beneficial to uniformity and spread maintenance, which are in the enhanced version of traditional ε dominance. 参考文献 相似文献 引证文献

Style adaptive contour tracking of human gait using explicit manifold models

In the domain of human motion analysis, the observed contours of the body contain rich information about the body configuration, the motion performed, the person’s identity, and even the emotional states of the person. In this paper, we introduce a framework for Bayesian tracking of the dynamic contours of the articulated human motion. We propose a factorized generative model for walking shape contour sequences that separates the dynamic deformation due to a motion, from the static variability due to the appearance of the person performing that motion. This results in an efficient tracking of gait sequence with a low-dimensional representation of body configuration and simultaneous adaptation to highly nonlinear static and dynamic shape deformations. Experimental results using the CMU Mobo, the University of Southampton (UoS), and M. Black’s walking sequence, show accurate contour tracking of a person walking by dynamic body configuration estimation on a low-dimensional manifold space and personal style estimation to fit the contour to the individual characteristics. In addition, the estimated shape style provides a good descriptors for human identification from gait.

Enhancing Human Face Detection by Resampling Examples Through Manifolds

As a large-scale database of hundreds of thousands of face images collected from the Internet and digital cameras becomes available, how to utilize it to train a well-performed face detector is a quite challenging problem. In this paper, we propose a method to resample a representative training set from a collected large-scale database to train a robust human face detector. First, in a high-dimensional space, we estimate geodesic distances between pairs of face samples/examples inside the collected face set by isometric feature mapping (Isomap) and then subsample the face set. After that, we embed the face set to a low-dimensional manifold space and obtain the low-dimensional embedding. Subsequently, in the embedding, we interweave the face set based on the weights computed by locally linear embedding (LLE). Furthermore, we resample nonfaces by Isomap and LLE likewise. Using the resulting face and nonface samples, we train an AdaBoost-based face detector and run it on a large database to collect false alarms. We then use the false detections to train a one-class support vector machine (SVM). Combining the AdaBoost and one-class SVM-based face detector, we obtain a stronger detector. The experimental results on the MIT + CMU frontal face test set demonstrated that the proposed method significantly outperforms the other state-of-the-art methods.