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Готові списки джерел за темами / Locality preserving projection / Статті в журналах

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Статті в журналах з теми "Locality preserving projection"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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1

Wong, W. K., and H. T. Zhao. "Supervised optimal locality preserving projection." Pattern Recognition 45, no. 1 (January 2012): 186–97. http://dx.doi.org/10.1016/j.patcog.2011.05.014.

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2

Yin, Jun, and Shiliang Sun. "Multiview Uncorrelated Locality Preserving Projection." IEEE Transactions on Neural Networks and Learning Systems 31, no. 9 (September 2020): 3442–55. http://dx.doi.org/10.1109/tnnls.2019.2944664.

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3

Xu, Yong, Binglei Xie, and Jingyu Yang. "Theoretical analysis of locality preserving projection and a fast orthogonal locality preserving projection algorithm." Journal of Electronic Imaging 21, no. 3 (September 14, 2012): 033024–1. http://dx.doi.org/10.1117/1.jei.21.3.033024.

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4

Zhang, Qi Rong, and Zhong Shi He. "Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition." Advanced Materials Research 121-122 (June 2010): 391–98. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.391.

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Анотація:

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

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5

Sun, Chuang, Zhousuo Zhang, Zhengjia He, Zhongjie Shen, Binqiang Chen, and Wenrong Xiao. "Novel method for bearing performance degradation assessment – A kernel locality preserving projection-based approach." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 3 (April 24, 2013): 548–60. http://dx.doi.org/10.1177/0954406213486735.

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Анотація:

Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

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6

Li, Jun-Bao, Jeng-Shyang Pan, and Shu-Chuan Chu. "Kernel class-wise locality preserving projection." Information Sciences 178, no. 7 (April 2008): 1825–35. http://dx.doi.org/10.1016/j.ins.2007.12.001.

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7

Guo Jinyu, 郭金玉, and 苑玮琦 Yuan Weiqi. "Palmprint Recognition Based on Locality Preserving Projection." Acta Optica Sinica 28, no. 10 (2008): 1920–24. http://dx.doi.org/10.3788/aos20082810.1920.

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8

Liang, Chunyan, Wei Cao, and Shuxin Cao. "Locality Preserving Discriminant Projection for Speaker Verification." Journal of Computer and Communications 08, no. 11 (2020): 14–22. http://dx.doi.org/10.4236/jcc.2020.811002.

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9

Long, Tianhang, Yanfeng Sun, Junbin Gao, Yongli Hu, and Baocai Yin. "Locality preserving projection based on Euler representation." Journal of Visual Communication and Image Representation 70 (July 2020): 102796. http://dx.doi.org/10.1016/j.jvcir.2020.102796.

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10

Shikkenawis, Gitam, and Suman K. Mitra. "On some variants of locality preserving projection." Neurocomputing 173 (January 2016): 196–211. http://dx.doi.org/10.1016/j.neucom.2015.01.100.

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11

Chen, Yu, Xiao-hong Xu, and Jian-huang Lai. "Optimal locality preserving projection for face recognition." Neurocomputing 74, no. 18 (November 2011): 3941–45. http://dx.doi.org/10.1016/j.neucom.2011.07.023.

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12

Xu, Jie, and Shengli Xie. "Recursive locality preserving projection for feature extraction." Soft Computing 20, no. 10 (June 21, 2015): 4099–109. http://dx.doi.org/10.1007/s00500-015-1745-y.

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13

ZHANG, TAIPING, BIN FANG, YUAN Y. TANG, and ZHAOWEI SHANG. "LOCALITY PRESERVING NONNEGATIVE MATRIX FACTORIZATION WITH APPLICATION TO FACE RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 05 (September 2010): 835–46. http://dx.doi.org/10.1142/s0219691310003730.

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Анотація:

In this paper, we propose a Locality Preserving Nonnegative Matrix Factorization (LPNMF) method to discover the manifold structure embedded in high-dimensional face space that is applied for face recognition. It is done by incorporating locality preserving constraints inside the cost function of NMF, then a new decomposition of a face with locality preserving can be obtained. As a result, the proposed LPNMF method shares some properties with the Locality Preserving Projection (LPP) such that it can effectively discover the manifold structure embedded in a high-dimensional face space. Experimental results show that LPNMF provides a better representation and achieves higher recognition rates in face recognition.

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14

Yin, Shuai, Yanfeng Sun, Junbin Gao, Yongli Hu, Boyue Wang, and Baocai Yin. "Robust Image Representation via Low Rank Locality Preserving Projection." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–22. http://dx.doi.org/10.1145/3434768.

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Анотація:

Locality preserving projection (LPP) is a dimensionality reduction algorithm preserving the neighhorhood graph structure of data. However, the conventional LPP is sensitive to outliers existing in data. This article proposes a novel low-rank LPP model called LR-LPP. In this new model, original data are decomposed into the clean intrinsic component and noise component. Then the projective matrix is learned based on the clean intrinsic component which is encoded in low-rank features. The noise component is constrained by the ℓ 1 -norm which is more robust to outliers. Finally, LR-LPP model is extended to LR-FLPP in which low-dimensional feature is measured by F-norm. LR-FLPP will reduce aggregated error and weaken the effect of outliers, which will make the proposed LR-FLPP even more robust for outliers. The experimental results on public image databases demonstrate the effectiveness of the proposed LR-LPP and LR-FLPP.

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15

ZHANG, Zhao, Ning YE, and Qiao-Lin YE. "Locality Preserving Multi-Projection Vector Fisher Discriminant Analysis." Chinese Journal of Computers 33, no. 5 (May 26, 2010): 865–76. http://dx.doi.org/10.3724/sp.j.1016.2010.00865.

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16

GONG, Qu, and Tao-tao HUA. "Face recognition based on improved locality preserving projection." Journal of Computer Applications 32, no. 2 (March 15, 2013): 528–30. http://dx.doi.org/10.3724/sp.j.1087.2012.00528.

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17

LI, Xiao-man, and Jing WANG. "Supervised locality preserving projection based on class information." Journal of Computer Applications 32, no. 2 (March 15, 2013): 531–34. http://dx.doi.org/10.3724/sp.j.1087.2012.00531.

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18

Lu, Pengli, and Xingbin Jiang. "Face Recognition Using Fuzzy Discriminant Locality Preserving Projection." Information Technology Journal 12, no. 17 (August 15, 2013): 4340–45. http://dx.doi.org/10.3923/itj.2013.4340.4345.

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19

Zhang, Liang, Shu-guang Huang, and Hao Guo. "A Fast Kernel Supervised Locality Preserving Projection Algorithm." Journal of Electronics & Information Technology 33, no. 5 (May 12, 2011): 1049–54. http://dx.doi.org/10.3724/sp.j.1146.2010.01044.

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20

Shikkenawis, Gitam, and Suman K. Mitra. "2D Orthogonal Locality Preserving Projection for Image Denoising." IEEE Transactions on Image Processing 25, no. 1 (January 2016): 262–73. http://dx.doi.org/10.1109/tip.2015.2501753.

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21

Huang, Shucheng, and Lu Zhuang. "Exponential Discriminant Locality Preserving Projection for face recognition." Neurocomputing 208 (October 2016): 373–77. http://dx.doi.org/10.1016/j.neucom.2016.02.063.

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22

Yang, Yifang, Yuping Wang, and Xingsi Xue. "Discriminant sparse locality preserving projection for face recognition." Multimedia Tools and Applications 76, no. 2 (January 20, 2016): 2697–712. http://dx.doi.org/10.1007/s11042-015-3212-2.

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23

Qi, Yong Feng, and Yuan Lian Huo. "Locality Preserving Maximum Scatter Difference Projection for Face Recognition." Applied Mechanics and Materials 411-414 (September 2013): 1179–84. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1179.

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Анотація:

Maximum Scatter Difference (MSD) aims to preserve discriminant information of sample space, but it fails to find the essential structure of the samples with nonlinear distribution. To overcome this problem, an efficient feature extraction method named as Locality Preserving Maximum Scatter Difference (LPMSD) projection is proposed in this paper. The new algorithm is developed based on locality preserved embedding and MSD criterion. Thus, the proposed LPMSD not only preserves discriminant information of sample space but also captures the intrinsic submanifold of sample space. Experimental results on ORL, Yale and CAS-PEAL face database indicate that the LPMSD method outperforms the MSD, MMSD and LDA methods under various experimental conditions.

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24

Gong, Wei Yu, and Fang Xia Lu. "Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition." Applied Mechanics and Materials 721 (December 2014): 766–70. http://dx.doi.org/10.4028/www.scientific.net/amm.721.766.

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For the problem of features extraction and dimensionality reduction of expression recognition, the paper proposes Gabor Locality Preserving Discriminant Projection (GLPDP) algorithm, which is based on Gabor Wavelet. Firstly, we use Gabor wavelet transform to have an expression feature extraction. Secondly, we improved the locality preserving projection (LPP) algorithm, introducing scatter difference in the LPP objective function to increase divergence constraints among the sample classes and extracts more discriminated features while having the dimensionality reduction. Finally, we use the nearest neighbor classifier to have a classification for expression category. The effectiveness of the proposed methods is validated through the experimental results on JAFFE and Cohn-Kanade Facial expression databases.

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25

Chu, Yonghe, Hongfei Lin, Liang Yang, Yufeng Diao, Dongyu Zhang, Shaowu Zhang, Xiaochao Fan, Chen Shen, and Deqin Yan. "Globality-Locality Preserving Maximum Variance Extreme Learning Machine." Complexity 2019 (May 2, 2019): 1–18. http://dx.doi.org/10.1155/2019/1806314.

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Анотація:

An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. Therefore, we propose a globality-locality preserving maximum variance extreme learning machine (GLELM) based on manifold learning. Based on the characteristics of the traditional ELM method, GLELM introduces the basic principles of linear discriminant analysis (LDA) and local preservation projection (LPP) into ELM, fully taking account of the discriminant information contained in the sample. This method can preserve the global and local manifold structures of data to optimize the projection direction of the classifier. Experiments on several widely used image databases and UCI datasets validate the performance of GLELM. The experimental results show that the proposed model achieves promising results compared to several state-of-the-art ELM algorithms.

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26

XUE, Si-zhong, Rui TAN, and Xiu-hong CHEN. "Method of kernel-based semi-supervised locality preserving projection." Journal of Computer Applications 32, no. 8 (May 7, 2013): 2235–37. http://dx.doi.org/10.3724/sp.j.1087.2012.02235.

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27

Zhao, Zhen-hua, and Xiao-hong Hao. "Linear Locality Preserving and Discriminating Projection for Face Recognition." Journal of Electronics & Information Technology 35, no. 2 (February 18, 2014): 463–67. http://dx.doi.org/10.3724/sp.j.1146.2012.00601.

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28

YAN, Jingjie, Wenming ZHENG, Minghai XIN, and Jingwei YAN. "Facial Expression Recognition Based on Sparse Locality Preserving Projection." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, no. 7 (2014): 1650–53. http://dx.doi.org/10.1587/transfun.e97.a.1650.

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29

WAN, Jian-Wu, Ming YANG, Gen-Lin JI, and Yin-Juan CHEN. "Weighted Cost Sensitive Locality Preserving Projection for Face Recognition." Journal of Software 24, no. 5 (December 28, 2013): 1155–64. http://dx.doi.org/10.3724/sp.j.1001.2013.04263.

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30

Li, Bin. "Corresponding Block Based Graph Construction for Locality Preserving Projection." Journal of Information and Computational Science 11, no. 11 (July 20, 2014): 3967–74. http://dx.doi.org/10.12733/jics20104220.

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31

Chen, Wei-Jie, Chun-Na Li, Yuan-Hai Shao, Ju Zhang, and Nai-Yang Deng. "2DRLPP: Robust two-dimensional locality preserving projection with regularization." Knowledge-Based Systems 169 (April 2019): 53–66. http://dx.doi.org/10.1016/j.knosys.2019.01.022.

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32

CHEN, WEN-SHENG, WEI WANG, JIAN-WEI YANG, and YUAN YAN TANG. "SUPERVISED REGULARIZATION LOCALITY-PRESERVING PROJECTION METHOD FOR FACE RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 06 (November 2012): 1250053. http://dx.doi.org/10.1142/s0219691312500531.

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Анотація:

Locality-preserving projection (LPP) is a promising manifold-based dimensionality reduction and linear feature extraction method for face recognition. However, there exist two main issues in traditional LPP algorithm. LPP does not utilize the class label information at the training stage and its performance will be affected for classification tasks. In addition, LPP often suffers from small sample size (3S) problem, which occurs when the dimension of input pattern space is greater than the number of training samples. Under this situation, LPP fails to work. To overcome these two limitations, this paper presents a novel supervised regularization LPP (SRLPP) approach based on a supervised graph and a new regularization strategy. It theoretically proves that regularization matrix [Formula: see text] approaches to the original one as the regularized parameter tends to zero. The proposed SRLPP method is subsequently applied to face recognition. The experiments are conducted on two publicly available face databases, namely ORL database and FERET database. Compared with some existing LDA-based and LPP-based linear feature extraction approaches, experimental results show that our SRLPP approach gives superior performance.

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33

Zhang, Qi, Kuiying Deng, and Tianguang Chu. "Sparsity induced locality preserving projection approaches for dimensionality reduction." Neurocomputing 200 (August 2016): 35–46. http://dx.doi.org/10.1016/j.neucom.2016.03.019.

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34

Zhong, Fujin, Defang Li, and Jiashu Zhang. "Robust locality preserving projection based on maximum correntropy criterion." Journal of Visual Communication and Image Representation 25, no. 7 (October 2014): 1676–85. http://dx.doi.org/10.1016/j.jvcir.2014.08.004.

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35

Shao, Yu. "Supervised global-locality preserving projection for plant leaf recognition." Computers and Electronics in Agriculture 158 (March 2019): 102–8. http://dx.doi.org/10.1016/j.compag.2019.01.022.

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36

Zhang, Honggang, Weihong Deng, Jun Guo, and Jie Yang. "Locality preserving and global discriminant projection with prior information." Machine Vision and Applications 21, no. 4 (August 12, 2009): 577–85. http://dx.doi.org/10.1007/s00138-009-0213-z.

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37

He, Lin, Xianjun Chen, Jun Li, and Xiaofeng Xie. "Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification." Applied Sciences 9, no. 10 (May 27, 2019): 2161. http://dx.doi.org/10.3390/app9102161.

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Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.

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38

Xi Chen, Jiashu Zhang, and Defang Li. "Direct Discriminant Locality Preserving Projection With Hammerstein Polynomial Expansion." IEEE Transactions on Image Processing 21, no. 12 (December 2012): 4858–67. http://dx.doi.org/10.1109/tip.2012.2219542.

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39

Shikkenawis, Gitam, and Suman K. Mitra. "Image denoising using 2D orthogonal locality preserving discriminant projection." IET Image Processing 14, no. 3 (February 28, 2020): 552–60. http://dx.doi.org/10.1049/iet-ipr.2019.0436.

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40

Guo, Huijie, Hui Zou, and Junyan Tan. "Semi-supervised dimensionality reduction via sparse locality preserving projection." Applied Intelligence 50, no. 4 (January 8, 2020): 1222–32. http://dx.doi.org/10.1007/s10489-019-01574-6.

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41

Jumahong, Huxidan, and Gulnaz Alimjan. "Face Recognition Based on Rearranged Modular Two-Dimensional Locality Preserving Projection." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 12 (August 27, 2018): 1856016. http://dx.doi.org/10.1142/s0218001418560165.

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Анотація:

This paper proposes a novel algorithm for feature extraction for face recognition, namely the rearranged modular two-dimensional locality preserving projection (Rm2DLPP). In the proposed algorithm, the original images are first divided into modular blocks, then the subblocks are rearranged to form two-dimensional matrices and finally the two-dimensional locality preserving projection algorithm is applied directly on the arranged matrices. The advantage of the Rm2DLPP algorithm is that it can utilize the local block features and global spatial structures of 2D face images simultaneously. The performance of the proposed method is evaluated and compared with other face recognition methods on the ORL, AR and FERET databases. The experimental results demonstrate the effectiveness and superiority of the proposed approach.

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42

Peng, Yanfeng, Yanfei Liu, Junsheng Cheng, Yu Yang, Kuanfang He, Guangbin Wang, and Yi Liu. "Remaining useful life prediction of rolling bearing using adaptive sparsest narrow-band decomposition and locality preserving projections." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401988977. http://dx.doi.org/10.1177/1687814019889771.

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Анотація:

There are two difficulties in the remaining useful life prediction of rolling bearings. First, the vibration signals are always interfered by noise signals. Second, some of the extracted features include useless information which may decrease the prediction accuracy. In order to solve the problems above, corresponding methods are employed in this article. First, adaptive sparsest narrow-band decomposition is utilized for extracting the degradation information from noise. Compared with the commonly used empirical mode decomposition method, problems including mode mixture and boundary effect caused by the calculation of extremas is not required. Second, locality-preserving projection is applied for merging the meaningful information from the original data and reduces the dimension of features. Based on adaptive sparsest narrow-band decomposition and locality preserving projection, a novel approach is employed for the remaining useful life prediction. The prediction procedure is as follows. First, the signals are analyzed by adaptive sparsest narrow-band decomposition and the feature vectors are constructed. Afterwards, the features are fused by locality preserving projection to merge useful information from the features. Least squares support vector machine is applied for the remaining useful life prediction in the end. The analysis results indicate that the proposed approach is reliable for rolling bearing remaining useful life prediction.

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43

Luo, Huiwu, Yuan Yan Tang, Chunli Li, and Lina Yang. "Local and Global Geometric Structure Preserving and Application to Hyperspectral Image Classification." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/917259.

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Анотація:

Locality Preserving Projection (LPP) has shown great efficiency in feature extraction. LPP captures the locality by theK-nearest neighborhoods. However, recent progress has demonstrated the importance of global geometric structure in discriminant analysis. Thus, both the locality and global geometric structure are critical for dimension reduction. In this paper, a novel linear supervised dimensionality reduction algorithm, calledLocality and Global Geometric Structure Preserving(LGGSP) projection, is proposed for dimension reduction. LGGSP encodes not only the local structure information into the optimal objective functions, but also the global structure information. To be specific, two adjacent matrices, that is, similarity matrix and variance matrix, are constructed to detect the local intrinsic structure. Besides, a margin matrix is defined to capture the global structure of different classes. Finally, the three matrices are integrated into the framework of graph embedding for optimal solution. The proposed scheme is illustrated using both simulated data points and the well-known Indian Pines hyperspectral data set, and the experimental results are promising.

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44

Zhu, Ting Ting, Li Na Wang, Yu Fu, and Yan Zhen Ren. "JPEG Steganalysis Based on Locality Preserving Projection Dimensionality Reduction Method." Applied Mechanics and Materials 411-414 (September 2013): 1185–88. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1185.

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In this paper, a JPEG steganalysis algorithm based on locality preserving projection (LPP) dimensionality reduction method is proposed for detecting the unseen stego algorithms. The co-occurrence features are extracted from DCT-DWT domain and dimension is reduced by using the LPP method. For improving the generalization capability of the algorithm, SVDD is used as the classifier. Experimental results reveal the fact that our scheme has better generalization capability and is more effective than others.

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45

YIN, Hai-bing, Zhao LIU, Ya-dong LIU, and De-wen HU. "Unsupervised spike extraction and classification based on locality preserving projection." Journal of Computer Applications 30, no. 9 (November 30, 2010): 2559–62. http://dx.doi.org/10.3724/sp.j.1087.2010.02559.

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