Locality Preserving Projections Research Articles

Locality Preserving Projections-Based Spatiotemporal Modeling of the Temperature Distribution of Lithium-Ion Batteries

The temperature distribution is one of the key factors affecting the safety, capacity fade, and optimal use of lithium-ion batteries (LIBs). However, it is a challenging task to develop an accurate approximation model for estimating this distribution, owing to the inherent nature of thermal processes, including time/space coupling, infinite dimensions, and strong nonlinearity. Therefore, this paper proposes a novel locality preserving projections (LPP)-based spatiotemporal modeling method to address this problem. First, an orthogonal enhanced LPP (OELPP) method is proposed for time/space decoupling. With this novel reduction dimension technique, the spatiotemporal temperature variables are separated into spatial basis functions (SBFs) and low-dimensional time variables. Then, to handle the temporal nonlinearity, a broad learning system with kernel-based manifold features (BLS-KMF) is designed to construct a dynamic temporal model. To improve the modeling performance, the kernel trick and the intrinsic manifold information are introduced to the proposed BLS-KMF to learn the mapped features. Finally, with the time/space synthesis, the battery temperature distribution can be reconstructed. Experimental results on a 32 Ah ternary lithium-ion battery illustrate the superior performance and effectiveness of the proposed method.

Fault detection and diagnosis for chemical processes using dynamic global–local preserving projections

AbstractMost traditional multivariate statistical monitoring methods require an assumption that the observation values at a certain moment and a past moment are statistically independent. However, in actual chemical and biological processes, the sample at a certain moment is often affected by the previous moment. Therefore, given the problem of more false alarms and poor detection ability based on the traditional principal component analysis, this article proposes a dynamic global–local preserving projections (DGLPP) algorithm. Unlike dynamic local preserving projections (DLPP) and dynamic principal component analysis (DPCA), DGLPP controls the global and local information retained in the dimensionality reduction data by introducing weight coefficients, which makes the algorithm applicable to more types of industrial processes. Moreover, new parameter determination methods are also proposed for improved detection and diagnosis. Through the improved contribution graph method, we can see the influence degree of each variable on the fault, to monitor and isolate the fault. Finally, by verifying the operation of the multivariable process and two practical cases, the results show that compared with DPCA, DLPP, and global local retained projection (GLPP) methods, the performance under this method has been significantly improved.

Fault Detection Algorithm Based on Dynamic Global–Local Preserving Projection

Industrial system operations usually have dynamic characteristics. If these characteristics are ignored, the performance of fault detection degrades. Herein, the fault-detection algorithm of dynamic global–local preserving projection (DGLPP) is employed to solve the problem mentioned. First, time-delay data are added to the sample to form an augmentation matrix and characterize the system dynamics. Second, the dimensionality of the augmented matrix is reduced using global–local preserving projection. The dimensionality-reduction method can preserve the data’s global and local structures. Then, a DGLPP model is built using the dimensionality-reduced data. Moreover, Hotelling’s T2 and squared prediction error (SPE) statistics are used for fault detection. Finally, this method is used to detect the fault in the Tennessee Eastman (TE) process. The experimental results show that the DGLPP method has an enhanced fault detection rate. Moreover, the fault-detection effects of the DGLPP method are better than those of the principal component analysis (PCA), local preserving projection (LPP), and global–local preserving projection (GLPP) methods.

Locality Preserving Projections with Autoencoder

Locality Preserving Projections (LPP) is a popular dimensionality reduction method in the manifold learning field. However, LPP and all its variants only consider the one-way mapping from the high-dimensional space to the low-dimensional space and have no reverse verification, resulting in inaccurate low-dimensional embeddings. In this paper, we propose a new LPP method, called LPPAE (Locality Preserving Projections with Autoencoder), based on the linear Autoencoder. It constructs a two-way mapping: at the encoding stage, the conventional projection of LPP is viewed as a mapping from the high-dimensional space to the low-dimensional space. At the decoding stage, the low-dimensional embeddings are mapped back to the original high-dimensional space. The main contributions of the new method are: (1) This design not only preserves the neighborhood relationship of the data but more importantly, ensures that the low-dimensional embeddings can more accurately ”represent” the original data, thus significantly improving the performance of LPP. Experimental results on Handwritten Alphadigits, COIL-20, Yale, AR datasets show that the recognition rates of LPPAE are 26.06, 10.09, 5.40, and 8.86% higher than those of the original LPP respectively. On the MNIST dataset, compared to some of the latest improvements of LPP, including LPPMDC, LAPP, LPP+TR, and DNLPP, the recognition rate of LPPAE has been improved by 12.50, 38.10, 9.10, and 2.61%, respectively. (2) LPPAE regards the conventional LPP as an encoder, which is a new perspective. The idea of LPPAE can be used as a general framework and then extended to other manifold learning methods, and then a series of new methods can be developed.

Dual subspace manifold learning based on GCN for intensity-invariant facial expression recognition

Facial expression recognition (FER) is one of the most important computer vision tasks for understanding human inner emotions. However, the poor generation ability of the FER model limits its applicability due to tremendous intraclass variation. Especially for expressions of varying intensities, the appearance differences among weak expressions are subtle, which makes FER tasks challenging. In response to these issues, this paper presents a dual subspace manifold learning method based on a graph convolutional network (GCN) for intensity-invariant FER tasks. Our method treats the target task as a node classification problem and learns the manifold representation using two subspace analysis methods: locality preserving projection (LPP) and peak-piloted locality preserving projection (PLPP). Inspired by the classic LPP, which maintains local similarity among data, this paper introduces a novel PLPP that maintains the locality between peak expressions and non-peak expressions to enhance the representation of weak expressions. This paper also reports two subspace fusion methods, one based on a weighted adjacency matrix and another on a self-attention mechanism, that combine the LPP and PLPP to further improve FER performance. The second method achieves a recognition accuracy of 93.83% on the CK+, 74.86% on the Oulu-CASIA and 75.37% on the MMI for weak expressions, outperforming state-of-the-art methods.

SCH: Symmetric Consistent Hashing for cross-modal retrieval

When handling large-scale multimodal data, hashing-based retrieval methods have gained significant attention due to their advantages in terms of low storage consumption and quick query speed. Despite the good performance of supervised hashing techniques, there are still certain limitations that need to be addressed. Firstly, they ignore valuable information such as reconstruction residuals when employing matrix factorization to learn the low-dimensional space. Secondly, most methods map heterogeneous data to the same feature space, which is unreasonable because different modalities possess distinct dimensions and distributions. Lastly, some methods relax constraints in order to optimize discrete hash codes, leading to considerable quantization errors. To solve the above problems, a novel two-step hashing-based technology called Symmetric Consistent Hashing (SCH) is proposed. Specifically, in the first step, two separate latent semantic spaces are learned by leveraging Principal Component Analysis to mitigate principal energy loss and Locality Preserving Projections to preserve local similarity. Then, the latent spaces of different modalities are aligned to facilitate the sharing of high-level semantic information by exploiting modality consistency. Next, hash codes are learned from distinct latent spaces, which are embedded in the second step for learning hash functions through Kernel Logistic Regression. To validate the effectiveness of SCH, extensive experiments on three publicly available benchmark datasets are accomplished. The results demonstrate the superiority of SCH over state-of-the-art hashing baselines, thus confirming the potency of the proposed approach.

Calibration transfer for near-infrared (NIR) spectroscopy based on local preserving projection

Calibration transfer is a mathematical or statistical technique to solve the universality of near-infrared (NIR) spectroscopy, which is particularly important in practical applications. Therefore, a calibration transfer method based on local preserving projection (CTLPP) to correct spectral differences was proposed in this paper. Local preserving projection (LPP) is a linear feature extraction algorithm, which is a linear approximation of Laplacian Eigenmap manifold learning. It can not only overcome the shortcomings of linear feature extraction algorithms which are difficult to maintain the nonlinear manifold of the original data, but also solve the problem that nonlinear manifold learning methods are difficult to directly map new samples, which is conducive to building the conversion relationship between the source spectra and the target spectra. The transfer performance of the proposed method was evaluated in three datasets, and compared with five approaches, namely, piecewise direct standardization (PDS), calibration transfer based on independent component analysis (CTICA), spectral space transformation (SST), principal components canonical correlation analysis (PC-CCA) and calibration transfer based on neighborhood preserving embedding (CTNPE). Experimental results indicated that this proposed method can successfully correct the detection spectra among different apparatus. In addition, it can obtain relatively low prediction root mean square error (RMSEP). Therefore, the comprehensive research conducted in this work demonstrated that the proposed method is a promising calibration transfer method in NIR applications, which can provide a reference when sharing the NIR spectral models with more and more instruments.

Two-Dimensional Exponential Sparse Discriminant Local Preserving Projections

The two-dimensional discriminant locally preserved projections (2DDLPP) algorithm adds a between-class weighted matrix and a within-class weighted matrix into the objective function of the two-dimensional locally preserved projections (2DLPP) algorithm, which overcomes the disadvantage of 2DLPP, i.e., that it cannot use the discrimination information. However, the small sample size (SSS) problem still exists, and 2DDLPP processes the whole original image, which may contain a large amount of redundant information in the retained features. Therefore, we propose a new algorithm, two-dimensional exponential sparse discriminant local preserving projections (2DESDLPP), to address these problems. This integrates 2DDLPP, matrix exponential function and elastic net regression. Firstly, 2DESDLPP introduces the matrix exponential into the objective function of 2DDLPP, making it positive definite. This is an effective method to solve the SSS problem. Moreover, it uses distance diffusion mapping to convert the original image into a new subspace to further expand the margin between labels. Thus more feature information will be retained for classification. In addition, the elastic net regression method is used to find the optimal sparse projection matrix to reduce redundant information. Finally, through high performance experiments with the ORL, Yale and AR databases, it is proven that the 2DESDLPP algorithm is superior to the other seven mainstream feature extraction algorithms. In particular, its accuracy rate is 3.15%, 2.97% and 4.82% higher than that of 2DDLPP in the three databases, respectively.

Robust Exponential Graph Regularization Non-Negative Matrix Factorization Technology for Feature Extraction

Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix factorization (NMF). Because Ga NMF regularizer is implemented by local preserving projections (LPP), there are small sample size problems (SSS). In view of the above problems, a new algorithm named robust exponential graph regularized non-negative matrix factorization (REGNMF) is proposed in this paper. By adding a matrix exponent to the regularizer of GNMF, the possible existing singular matrix will change into a non-singular matrix. This model successfully solves the problems in the above algorithm. For the optimization problem of the REGNMF algorithm, we use a multiplicative non-negative updating rule to iteratively solve the REGNMF method. Finally, this method is applied to AR, COIL database, Yale noise set, and AR occlusion dataset for performance test, and the experimental results are compared with some existing methods. The results indicate that the proposed method is more significant.

Facial Recognition using Locality Preserving Projection Algorithm

Facial Recognition Technology (FRT) has become the research object of many in the recent times considering the challenges with enormous increase in human population. The face as the main axis in social relations plays an important role in the representation of human identity, which requires an increased level of security and the creation of exchange tricks for safe and recognizable evidence and innovations of individual authentication. Apparently, facial recognition is widely adopted for security reasons, and the uniqueness of human characteristics has increased the popularity of facial recognition technology systems worldwide. Law enforcement agencies faced the problem of the impossibility of proper investigation of criminal cases. Suspects are often difficult to catch and the wrong persons may be arrested due to the basis and methods adopted for the investigation. This study proposes a facial recognition system for the identification of criminals using Locality Preserving Projection (LPP) Algorithm. The development of the system involves specification of the functions resulting from the performance analysis obtained from other related systems, and the translation of the developed model into the design of the proposed system. The framework was evaluated by matching the faces of people extracted through special cameras with the images of people on a watch list. Watch lists that contain images of people, including people not suspected of wrong doings. Strategically, the implementation of the facial recognition system was achieved with Locality Preserving Projection (LPP) Algorithm to improve the feature extraction methods and dimensionality reduction techniques. Cascading Style Sheets (CSS) was used for describing the presentation of the document written in HTML language and JavaScript for the front-end for optimum web compatibility. A Python binding of the cross-platform Qt GUI toolkit and Python plugin were used to implement the graphical user interface for detecting and recognizing images and the presence of an individual upon entering the database library. Interestingly, the developed framework recorded accuracy and recognition >95% capacity under normal conditions, including lighting and distance from camera and at a reasonably cheaper cost in comparison with previously proposed Facial Recognition Technology

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.

Improved Locality Preserving Projections Based on Heat-Kernel and Cosine Weights for Fault Classification in Complex Industrial Processes

Data-driven fault diagnosis techniques have been widely used in industrial processes. However, facing a large amount of high-dimensional, nonlinear, and strongly coupled process data, traditional data-driven methods achieve low diagnostic accuracy due to ignoring the structural features inside data. To overcome this problem, this article proposes an improved locality preserving projections based on the heat-kernel and cosine weight matrix named Heat-Kernel and Cosine Weights Locality Preserving Projections (HC-LPP). In HC-LPP, a novel weight matrix construction strategy is employed, where a heat-kernel function is combined with a cosine function to optimize the weight matrix between data in terms of shortening distance and angle correlation, respectively. With the new weight matrix, the proposed HC-LPP considers both the distance and the correlation of samples (the shorter the distance is, the closer the neighbors are; the smaller the angle is, the more similar the neighbors are). The dimensionality reduction process of HC-LPP can well maintain the spatial geometric structure of data. Finally, the proposed HC-LPP integrated with the AdaBoost. M2 classifier is applied to the Tennessee Eastman process and the PROcess NeTwork Optimization process for fault diagnosis performance verification. Simulation results show, the proposed HC-LPP achieves better performance in diagnostic accuracy compared with other related methods.

Novel Discriminant Locality Preserving Projection Integrated With Monte Carlo Sampling for Fault Diagnosis

In complex industrial processes, the technique of fault diagnosis has been playing an increasingly considerable role in ensuring the safety of life and property. Unfortunately, the process data of complex industrial processes have the features of high dimension. Feature extraction from high-dimensional data is promising to coping with the fault data with high dimension. Recently, one of manifold learning methods named discriminant locality preserving projection achieves excellent performance in feature extraction. However, the performance of discriminant locality preserving projection (DLPP) is subject to the problem of matrix decomposition in the denominator of the objection function caused by the small sample size (SSS) issue. To overcome this limitation, novel DLPP integrated with Monte Carlo sampling is proposed to enhance the performance of feature extraction through dimensionality reduction. In the proposed MC-DLPP, Monte Carlo sampling is first utilized to generate fault samples for each fault type. With the aid of the virtually generated fault samples, the rank of the matrix in the denominator of the objection function of DLPP increases, thus well addressing the SSS problem. The Softmax classifier is used for fault diagnosis. To test the performance of the improved DLPP-based fault diagnosis, case studies using the Tennessee Eastman process are carried out. Simulation results confirm the presented MC-DLPP achieves superior accuracy in fault diagnosis.

Fast orthogonal locality-preserving projections for unsupervised feature selection

Graph-based sparsity learning is one of the most successful unsupervised feature selection methods that has been widely adopted in many real-world applications. However, traditional graph-based unsupervised feature selection methods have several drawbacks: (1) being time-consuming and unable to deal with large-scale problems; (2) having difficulty tuning the regularization parameter with the sparsity regularization term; and (3) being unable to find explicit solutions owing to the limitation of sparsity, that is, feature selection with the ℓ2,1-norm constrained problem. Thus, this paper proposes OLPPFS, a method to preserve the local geometric structure within the feature subspace by imposing the ℓ2,0-norm constraint. First, the linear mapping capability of the proposed model is enhanced using locality-preserving projections (LPPs), whichpreserve the local and global geometric manifold structure of the data while enhancing the ability to reconstruct data. Second, the graph-embedding learning method can accelerate the construction of a sparsity affinity graph and describe the intrinsic structure of the dataset well. More importantly, we propose a method for solving a projection matrix with the ℓ2,0-norm constrained, which can accurately select a explicit group of discriminative feature subsets. This method can yield a more accurate sparse projection matrix than the ℓ2,1-norm. We also adopt FOLPPFS, an effective anchor-based strategy to further accelerate our model with two flexible options. Extensive experiments on eight datasets demonstrate that the proposed method is superior to the other methods and can preserve a better local geometric structure of the dataset with less time consumption.

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.

Short-term Power Load Forecasting Based on Orthogonal PCA-LPP Dimension Reduction and IGWO-BiLSTM

Background: Accurate power load forecasting is of great significance in ensuring power load planning, reliability and economic operation. The traditional power load is easy to be affected by climate, environment, date type and other factors, resulting in the problem of poor forecasting accuracy. Therefore, it is necessary to study power load forecasting. Objective: Through machine learning, dimension reduction method and intelligent optimization algorithm, the accuracy of load forecasting is improved Methods: In order to fully extract load information and improve the accuracy of short-term load forecasting for campus electricity, an improved combination of orthogonal dimensionality reduction and Bilstm is proposed to optimize the hyperparameters in BiLSTM using an improved gray wolf algorithm. Firstly, using the advantages of principal component analysis (PCA) and Locality Preserving Projection (LPP) to maintain the global and local structure of the data, respectively, the Orthogonal PCA-LPP（OPCA-LPP） dimensionality reduction method is proposed to reduce the dimensionality of the original multidimensional data. Finally, the dimensionality-reduced data is used as the input of BiLSTM and optimized by the improved Gray Wolf algorithm, which can enhance the prediction capability of the model and thus achieve accurate prediction of short-term electric load. Results: The Mae and RMSE of this paper are 1.6585 and 1.7602 respectively. The results show that the method proposed in this paper is reasonable Conclusion: This method is applied to power load forecasting. The comparative experimental results show that this method reduces the dimension of data input, simplifies the complexity of network input data, and improves the accuracy of load forecasting. Compared with other methods, it can effectively improve the accuracy of load forecasting, and provide a basis for formulating reasonable power grid operation mode and balanced dispatching of power grid.

Improved dynamic kernel PCA based on local preserving projections and its application for electric submersible pump fault diagnosis

AbstractDynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as the T2 and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.

Linear dimensionality reduction method based on topological properties

Dimensionality reduction is an important data preprocessing technique that has been extensively studied in machine learning and data mining. Locality Preserving Projection (LPP) is a widely used linear unsupervised dimensionality reduction method, which maps high-dimensional data into low-dimensional subspace through linear transformation. Although various variants of LPP have been proposed to tackle different drawbacks of LPP, it is identified in this article that LPP does not possess the important topological property of translation invariance, that is, the linear transformation given by LPP is strongly related to the relative position between the data and the origin of the coordinate system. In this article, we theoretically analyze the reason why this drawback exists in LPP and propose to resolve it by introducing a kind of centralization to the model. Moreover, as topological properties are prominent information to characterize the structure of the data, this article proposes a further improvement of LPP to maintain topological connectivity of data after dimensionality reduction. Experiments on multiple synthetic and real-world datasets show that the new model incorporating topological properties outperforms not only the original LPP model but also several other classic linear or non-linear dimensionality reduction methods.

Perceptual Image Hashing With Locality Preserving Projection for Copy Detection

Perceptual image hashing is an effective and efficient way to identify images in large-scale databases, where two major performances are robustness and discrimination. A better tradeoff between robustness and discrimination is still a severe challenge for the current hashing research. Aiming at this issue, we design a novel perceptual image Hashing with Locality Preserving Projection (LPP) (hereafter HLPP). Specifically, to improve the robustness against content-preserving operations, Gabor filtering is leveraged to adaptively extract the orientation and structure features, which are consistent with the response of human visual system. The LPP is adopted to learn intrinsic local structure from the maximum Gabor filtering response. The use of LPP can discover meaningful low-dimensional information hidden in the maximum Gabor filtering response and thus improves discrimination of HLPP. During hash similarity calculation, the Hamming distance is selected as the metric. The tradeoff performance between robustness and discrimination is validated on benchmark databases, and the results indicate that the proposed HLPP is superior to some state-of-the-art algorithms. In addition, extensive experiments of copy detection also demonstrate that the proposed HLPP can provide higher accuracy than the compared algorithms.

3D Face Reconstruction from a Single Image Using a Combined PCA-LPP燤ethod

In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance. Principal component analysis (PCA) is commonly used to compress images and extract features. One disadvantage of PCA is local feature loss. To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP). However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering. In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques. By contrast, in this study, complex and detailed parts, such as wrinkles around the eyes and mouth, can be reconstructed by composing the topology of the 3D face model as an adjacency graph and extracting local features from the connection relationship between the 3D model vertices. Experiments verified the effectiveness of the proposed method. When the proposed method was applied to the 3D face reconstruction evaluation set, a performance improvement of 10% to 20% was observed compared with the existing PCA-based method.