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Journal articles on the topic 'Local Discriminant Bases'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Saito, Naoki, and Ronald R. Coifman. "Local discriminant bases and their applications." Journal of Mathematical Imaging and Vision 5, no. 4 (December 1995): 337–58. http://dx.doi.org/10.1007/bf01250288.

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2

Strauss, Daniel J., Gabriele Steidl, and Wolfgang Delb. "Feature extraction by shape-adapted local discriminant bases." Signal Processing 83, no. 2 (February 2003): 359–76. http://dx.doi.org/10.1016/s0165-1684(02)00420-6.

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3

Tafreshi, R., F. Sassani, H. Ahmadi, and G. Dumont. "Local discriminant bases in machine fault diagnosis using vibration signals." Integrated Computer-Aided Engineering 12, no. 2 (April 5, 2005): 147–58. http://dx.doi.org/10.3233/ica-2005-12202.

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4

Fossgaard, Eirik. "Alternative Local Discriminant Bases Using Empirical Expectation and Variance Estimation." Journal of Computational and Graphical Statistics 10, no. 2 (June 2001): 329–49. http://dx.doi.org/10.1198/10618600152628202.

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5

Umapathy, Karthikeyan, Sridhar Krishnan, and Raveendra K. Rao. "Audio Signal Feature Extraction and Classification Using Local Discriminant Bases." IEEE Transactions on Audio, Speech and Language Processing 15, no. 4 (May 2007): 1236–46. http://dx.doi.org/10.1109/tasl.2006.885921.

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6

He, Qingbo, Xiaoxi Ding, and Yuanyuan Pan. "Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/923424.

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Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

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Umapathy, K., and S. Krishnan. "Feature analysis of pathological speech signals using local discriminant bases technique." Medical & Biological Engineering & Computing 43, no. 4 (August 2005): 457–64. http://dx.doi.org/10.1007/bf02344726.

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8

Cui, Jianwei, Mengxiao Shan, Ruqiang Yan, and Yahui Wu. "Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/283718.

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This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

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Shirzhiyan, Zahra, Elham Shamsi, Amir Salar Jafarpisheh, and Amir Homayoun Jafari. "Objective classification of auditory brainstem responses to consonant-vowel syllables using local discriminant bases." Speech Communication 114 (November 2019): 36–48. http://dx.doi.org/10.1016/j.specom.2019.09.003.

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10

Umapathy, K., and S. Krishnan. "Modified Local Discriminant Bases Algorithm and Its Application in Analysis of Human Knee Joint Vibration Signals." IEEE Transactions on Biomedical Engineering 53, no. 3 (March 2006): 517–23. http://dx.doi.org/10.1109/tbme.2005.869787.

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11

Asensio-Cubero, Javier, John Q. Gan, and Ramaswamy Palaniappan. "Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing." Biomedical Signal Processing and Control 8, no. 6 (November 2013): 772–78. http://dx.doi.org/10.1016/j.bspc.2013.07.004.

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12

Akter, MM, MG Azom, MS Reza Sabuz, MH Islam, and MR Islam. "Sexual dimorphism of Canthophrys gongota (Teleostei: Cobitidae) using landmark-based geometric morphometrics in the Atrai river of Bangladesh." Bangladesh Journal of Scientific and Industrial Research 54, no. 2 (June 1, 2019): 187–94. http://dx.doi.org/10.3329/bjsir.v54i2.41676.

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Studies on sexual dimorphism of gongota Loach Canthophrys gongota (Local name: Pahari Gutum) was performed capturing them from the Atrai River of Dinajpur district in Bangladesh. Females had light blotches and patches with thick and rounded pectoral and pelvic fins while males having dark blotches and patches with thin and comparatively pointed paired fins. Body size, lengths of the anal fin and distances between the bases of pectoral, pelvic and caudal fins were significantly different (5.62 < F < 11.65, P ˂ 0.05) between the sexes of C. gongota. The expansion factors of mean thin-plate grids and vectors also showed that the head region of males was statistically different from females, whereas abdomen and tail of the females were considerably broader than those of the males. Both PCA (principal component analysis) and DFA (discriminant function analysis) plots showed morphologically little overlapping of landmark points which discriminated the females from the males. These findings are the first records on the sexual dimorphism of this rare species that would be baseline in a future study. Bangladesh J. Sci. Ind. Res.54(2), 187-194, 2019

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13

Hu, Peng, Dezhong Peng, Jixiang Guo, and Liangli Zhen. "Local feature based multi-view discriminant analysis." Knowledge-Based Systems 149 (June 2018): 34–46. http://dx.doi.org/10.1016/j.knosys.2018.02.008.

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14

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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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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15

Wang, Yong-mao, Zheng-guang Xu, and Shan Zhao. "Neighborhood Graph Embedding Based Local Adaptive Discriminant Projection." Journal of Electronics & Information Technology 35, no. 3 (January 20, 2014): 633–38. http://dx.doi.org/10.3724/sp.j.1146.2012.00793.

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16

Gao, Yunlong, Yisong Zhang, Jinyan Pan, Sizhe Luo, and Chengyu Yang. "Discriminant analysis based on reliability of local neighborhood." Expert Systems with Applications 175 (August 2021): 114790. http://dx.doi.org/10.1016/j.eswa.2021.114790.

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17

Chen, Caikou, Jun Shi, and Pu Huang. "Unsupervised Discriminant Analysis Based on the Local and Non-local Mean." Physics Procedia 24 (2012): 1967–73. http://dx.doi.org/10.1016/j.phpro.2012.02.289.

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18

Yu, Lu, Jun Xie, and Lei Zhu. "A Local Discriminant Projection Method Based on Objective Space." Journal of Electronics & Information Technology 33, no. 10 (October 15, 2011): 2390–95. http://dx.doi.org/10.3724/sp.j.1146.2010.00939.

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19

Huang, Hong, Jiamin Liu, Hailiang Feng, and Tongdi He. "Ear recognition based on uncorrelated local Fisher discriminant analysis." Neurocomputing 74, no. 17 (October 2011): 3103–13. http://dx.doi.org/10.1016/j.neucom.2011.04.022.

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20

Zhang, Shanwen, Wenzhun Huang, and Zhen Wang. "Plant species identification based on modified local discriminant projection." Neural Computing and Applications 32, no. 21 (October 11, 2018): 16329–36. http://dx.doi.org/10.1007/s00521-018-3746-0.

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21

Huang, Pu, Tao Li, Guangwei Gao, Yazhou Yao, and Geng Yang. "Collaborative representation based local discriminant projection for feature extraction." Digital Signal Processing 76 (May 2018): 84–93. http://dx.doi.org/10.1016/j.dsp.2018.02.009.

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22

Roh, Seok-Beom, Eun-Jin Hwang, and Tae-Chon Ahn. "Design of Pattern Classification Rule based on Local Linear Discriminant Analysis Classifier by using Differential Evolutionary Algorithm." Journal of Korean Institute of Intelligent Systems 22, no. 1 (February 25, 2012): 81–86. http://dx.doi.org/10.5391/jkiis.2012.22.1.81.

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23

Wang, Yong Mao. "An Image Retrieval Model Based on Local Fisher Discriminant Analysis." Advanced Materials Research 255-260 (May 2011): 2057–61. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2057.

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This paper introduces an image retrieval model based on dimensionality reduction. The proposed model is divided into two main techniques, the first one is concerned with the feature extraction from image database, and the second one is performing a dimensionality reduction. In the first technique, the color histogram and Color Texture Moment are used to extract the color and texture features, respectively. In the second technique, Local Fisher Discriminant Analysis (LFDA) which is a supervised linear dimensionality reduction algorithm is used to performing dimensionality. LFDA combines the ideas of Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). LFDA can preserve both manifold of data and discriminant information. Experiments demonstrate that the proposed image retrieval scheme based on dimensionality reduction can achieve satisfactory results.

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24

Lopes, Thais Cristina de Souza, Jeane Cruz Portela, Stefeson Bezerra de Melo, Valéria Nayara Silva de Oliveira, Rafael Oliveira Batista, Joaquim Emanuel Fernandes Gondim, and Maria Elidayane da Cunha. "Characterization of Physical-Chemical and Structural Soil Attributes in the Semiarid Region of the Rio Grande do Norte State, Brazil." Journal of Agricultural Science 11, no. 3 (February 15, 2019): 359. http://dx.doi.org/10.5539/jas.v11n3p359.

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The study of the soil characterization and the relation of its attributes allows a systematic proposal of the local particularities, leading to adequate practices for maintenance and/or preservation of its productive capacity. In this sense, the aim of this study was to evaluate the influence of structural attributes in association with physical and chemical soil classes, using the multivariate statistical techniques to differentiate environments. The research was carried out in the Moacir Lucena Project, located in the municipality of Apodi, RN, Brazil. Three representative environments were chosen as follows: Profile 1 (P1)-Red-yellow Latosol-Area in recovery (1AR), P2-Haplic Cambisol-Lake Area, (2AL) and P3-Eutrophic Yellow Latosol-Cashew Tree Area (3AC). The soil samples were collected in the horizons of the studied areas. Ten (10) samples were collected per horizons in volumetric rings and in soil blocks (aggregate analysis), which resulted in triplicates in the laboratory. Structural, physical and chemical attributes were evaluated. The data were analyzed using multivariate statistical techniques, with correlation matrix, clustering analysis and factorial analysis performed by the extraction of the factors into principal components. The use of clustering analysis allowed the formation of four groups for soil classes and attributes; the inorganic fractions were determinant for environmental differentiation, where the sand was discriminant for the Red-yellow Latosol and the Eutrophic Yellow Latosol, and the clay and silt for the Haplic Cambisol. Higher similarity was observed in the transition horizons of the Latosols Class. The physical and structural attributes were determinant in the dissimilarity for the Haplic Cambisol, reflecting in physical restrictions to the plant growth. The factor analysis revealed that the variables particle density (Dp), Ca2+, Mg2+, sum of bases (SB) and cation exchange capacity (CEC) for factor 1, followed by pH, P, K+, total Sand, Clay and soil density (Ds) for factor 2 are important soil attributes to distinguish the studied environments.

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25

HUANG, HONG, JIAMIN LIU, and HAILIANG FENG. "UNCORRELATED LOCAL FISHER DISCRIMINANT ANALYSIS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (September 2011): 863–87. http://dx.doi.org/10.1142/s0218001411008889.

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An improved manifold learning method, called Uncorrelated Local Fisher Discriminant Analysis (ULFDA), for face recognition is proposed. Motivated by the fact that statistically uncorrelated features are desirable for dimension reduction, we propose a new difference-based optimization objective function to seek a feature submanifold such that the within-manifold scatter is minimized, and between-manifold scatter is maximized simultaneously in the embedding space. We impose an appropriate constraint to make the extracted features statistically uncorrelated. The uncorrelated discriminant method has an analytic global optimal solution, and it can be computed based on eigen decomposition. As a result, the proposed algorithm not only derives the optimal and lossless discriminative information, but also guarantees that all extracted features are statistically uncorrelated. Experiments on synthetic data and AT&T, extended YaleB and CMU PIE face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of the proposed method.

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26

Zhang, Jian Xin, Zhang E. Zhang, and Jian Yang Liu. "Palmprint Recognition Based on Sparse Two-Dimensional Local Discriminant Projections." Advanced Materials Research 998-999 (July 2014): 894–98. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.894.

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A novel palmprint recognition method based on sparse two-dimensional local discriminant projections (S2DLDP) is proposed. After a description of the basic theory and resolution method for S2DLDP, the paper presents the detail palmprint feature extraction method based on S2DLDP, and tests the algorithm performance by various non-zero elements size and neighborhood size. S2DLDP considerers the class information, local separability, two-dimensional image inherent properties of training samples and sparse projection, which provides an intuitive, semantic and interpretable feature subspace for palmprint representation. The optimal recognition accuracy of EER=2.2% is obtained on PolyU palmprint database, which also illuminates the effectiveness of the proposed algorithm.

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27

Pan, Feng, Jiandong Wang, and Xiaohui Lin. "Local margin based semi-supervised discriminant embedding for visual recognition." Neurocomputing 74, no. 5 (February 2011): 812–19. http://dx.doi.org/10.1016/j.neucom.2010.11.004.

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28

Zhao, F. Z., H. Y. Liang, X. L. Wu, and D. H. Ding. "Region-based Active Contour Segmentation Model with Local Discriminant Criterion." International Journal of Security and Its Applications 11, no. 7 (July 31, 2017): 73–86. http://dx.doi.org/10.14257/ijsia.2017.11.7.06.

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29

CUI, LIMIN, YANTAO WEI, YUAN YAN TANG, and HONG LI. "GABOR-BASED TENSOR LOCAL DISCRIMINANT EMBEDDING AND ITS APPLICATION ON PALMPRINT RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 02 (March 2010): 327–42. http://dx.doi.org/10.1142/s0219691310003468.

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In this paper, a novel feature extraction method called Gabor-based tensor local discriminant embedding (GTLDE) is proposed. GTLDE first gets the high-order statistic information by using a biologically inspired hierarchical model, and then tensor local discriminant embedding (TLDE) is carried out to extract the discriminant features of the image for recognition task. The method we proposed is not only robust to local translation and scale variations, but also has high distinguishing ability. More importantly, our method can achieve high accuracy with a small number of training samples. Experimental results on PolyU-II palmprint database demonstrate the effectiveness of the method we proposed.

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30

Imran, Sajida, and Young-Bae Ko. "A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2976751.

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WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

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31

Lin, Yu E., and Yong Cun Guo. "Optimal Uncorrelated Unsupervised Discriminant Projection." Applied Mechanics and Materials 701-702 (December 2014): 54–57. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.54.

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Unsupervised Discriminant Projection (UDP) is a typical manifold-based dimensionality reduction method, and has been successfully applied in face recognition. However, UDP suffers from the small sample size problem and usually deteriorates because the basis vectors of UDP are statistically correlated. In order to resolve these problems, we propose an Optimal Uncorrelated Unsupervised Discriminant Projection (OUUDP).The aim of OUUDP is to seek a feature submanifold such that the local scatter is minimized and non-local scatter scatter is maximized simultaneously in the embedding space by using a difference-based optimization objective function. Moreover, we impose an appropriate constraint to make the extracted features statistically uncorrelated. As a result, OUUDP can solve the small sample size problem and exploit statistically uncorrelated features. Experimental results on ORL databases demonstrate the effectiveness of the proposed algorithm.

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32

Zhang, Shanwen, Chuanlei Zhang, and Xuqi Wang. "Plant species recognition based on global–local maximum margin discriminant projection." Knowledge-Based Systems 200 (July 2020): 105998. http://dx.doi.org/10.1016/j.knosys.2020.105998.

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33

Zhao, Jiuliang, Yishu Peng, and Yunhui Yan. "Steel Surface Defect Classification Based on Discriminant Manifold Regularized Local Descriptor." IEEE Access 6 (2018): 71719–31. http://dx.doi.org/10.1109/access.2018.2881962.

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34

Tang, Qiu, Yi Chai, Jianfeng Qu, and Xiaoyu Fang. "Industrial process monitoring based on Fisher discriminant global-local preserving projection." Journal of Process Control 81 (September 2019): 76–86. http://dx.doi.org/10.1016/j.jprocont.2019.05.010.

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35

Wang, Yintong, Jiandong Wang, Haiyan Chen, and Bo Sun. "Semi-Supervised Local Fisher Discriminant Analysis Based on Reconstruction Probability Class." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (February 27, 2015): 1550007. http://dx.doi.org/10.1142/s021800141550007x.

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Fisher discriminant analysis (FDA) is a classic supervised dimensionality reduction method in statistical pattern recognition. FDA can maximize the scatter between different classes, while minimizing the scatter within each class. As it only utilizes the labeled data and ignores the unlabeled data in the analysis process of FDA, it cannot be used to solve the unsupervised learning problems. Its performance is also very poor in dealing with semi-supervised learning problems in some cases. Recently, several semi-supervised learning methods as an extension of FDA have proposed. Most of these methods solve the semi-supervised problem by using a tradeoff parameter that evaluates the ratio of the supervised and unsupervised methods. In this paper, we propose a general semi-supervised dimensionality learning idea for the partially labeled data, namely the reconstruction probability class of labeled and unlabeled data. Based on the probability class optimizes Fisher criterion function, we propose a novel Semi-Supervised Local Fisher Discriminant Analysis (S2LFDA) method. Experimental results on real-world datasets demonstrate its effectiveness compared to the existing similar correlation methods.

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36

Li, Shuyi, Bob Zhang, Shuping Zhao, and Jinfeng Yang. "Local discriminant coding based convolutional feature representation for multimodal finger recognition." Information Sciences 547 (February 2021): 1170–81. http://dx.doi.org/10.1016/j.ins.2020.09.045.

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37

Cui, Yan, Chun-Hou Zheng, and Jian Yang. "Dimensionality reduction for microarray data using local mean based discriminant analysis." Biotechnology Letters 35, no. 3 (November 18, 2012): 331–36. http://dx.doi.org/10.1007/s10529-012-1092-3.

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38

Peng, Wen, Yan Ni Deng, Yuan Shi, Yuan Xing Lv, and Qiang Li. "Fusion Algorithm Based on Improved Linear Local Tangent Space Alignment Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 3447–50. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3447.

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A uncorrelated adaptive discriminant linear local tangent space alignment (UDALLTSA) is proposed based on improved linear local tangent space alignment algorithm. The algorithm uses an adaptive neighborhood selection to select the appropriate neighborhood, and introduces curvature to amend the model, modifies the constraints of the objective function by use inter-class scatter matrix, and constraints on basis vectors to compute the best projection matrix. By comparing the results of the experiments, it shows that after integrating the discriminant information into the algorithm , uncorrelated constraints and adaptive neighborhood selection can well improve the recognition rate and robustness, thus, possessing good noise immunity, and eliminating redundant information of base vectors, make this fusion algorithm a supervised learning algorithm.

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van der Schaar, M., E. Delory, A. Català, and M. André. "Neural network-based sperm whale click classification." Journal of the Marine Biological Association of the United Kingdom 87, no. 1 (February 2007): 35–38. http://dx.doi.org/10.1017/s0025315407054756.

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Recordings of a group of foraging sperm whales usually result in a mixture of clicks from different animals. To analyse the click sequences of individual whales these clicks need to be separated, and for this an automatic classifier would be preferred. Here we study the use of a radial basis function network to perform the separation. The neural network's ability to discriminate between different whales was tested with six data sets of individually diving males. The data consisted of five shorter click trains and one complete dive which was especially important to evaluate the capacity of the network to generalize. The network was trained with characteristics extracted from the six click series with the help of a wavelet packet-based local discriminant basis. The selected features were separated in a training set containing 50 clicks of each data set and a validation set with the remaining clicks. After the network was trained it could correctly classify around 90% of the short click series, while for the entire dive this percentage was around 78%.

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Wu, Ya Hui, Meng Xiao Shan, Yu Ning Qian, Xin Liang Li, and Ru Qiang Yan. "Aeroengine Rub-Impact Fault Diagnosis Based on Wavelet Packet Transform and the Local Discriminate Bases." Applied Mechanics and Materials 226-228 (November 2012): 740–44. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.740.

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With the development of aeroengine towards the direction of high speed and high performance, the clearance between rotor and stator in aerongine is reduced so that the possibility of rub-impact fault is increased. Since rub-impact signals often exhibits non-stationarity, an integrated approach, which combines the wavelet packet transform (WPT) with local discriminate bases (LDB), is presented in this study to diagnose the rub-impact faults. Specifically, the LDB algorithm is used to select an optimal set of orthogonal time-frequency subspaces resulted from WPT, which have the best discriminatory information for aeroengine rub-impact fault classification. Then the desired parameters generated by the LDB vectors were taken as input to a Bayes classifier for identifying rub-impact faults. Experimental results from the aeroengine vibration signals show that the fault diagnosis method can classify working conditions and fault patterns effectively.

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41

Nosike, R. J., O. M. Obike, J. Ezea, E. N. Obasi, C. A. Ebuzor, D. N. Onunkwo, O. F. Nwakpu, S. N. Ibe, and U. K. Oke. "Discrimination of the Nigerian local turkeys into breeds using linear body measurements." Nigerian Journal of Animal Production 44, no. 1 (December 24, 2020): 54–60. http://dx.doi.org/10.51791/njap.v44i1.463.

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Turkey is one of the poultry species that is declining in Nigeria due to its importation as frozen turkey. In this study, a total of 78 day-old random-bred Nigerian local turkey poults were used to generate another 232 day-old poults to discriminate Nigerian local turkey based on linear body measurements. Three phenotypic classes (Black, White and Spotted) were obtained as base population and used to generate F1 progeny. Experimental design was a randomized complete block (RCBD) with phenotypic class as major factor of interest and hatch as block. Linear body measurements (LBMs), namely body length (BDL), shank length (SHL), keel length (KLL), breast width (BW), wing length (WGL) and drumstick length (DSL) were significantly different at weeks 7, 9, 11 and 19. There were two discriminant functions. The variance ratio (eigenvalue) of each of the two discriminant functions were not significant (p>0.05). The percentage of the total variance explained was 87.7% and 12.3% for the two functions respectively. Group centroids for the phenotypic classes were 7.210, -1.254 and -5.956 for Black, White and Spotted turkeys respectively. The magnitudes and signs of the group centroids indicate that the three phenotypes are distinctly different from one another when BDL (weeks 7, 11, 13 and 15), WGL (weeks 7, 9 and 11), KLL (weeks 7, 9 and 15) and SHL (weeks 11 and 21) are used as the discriminating factors. The study shows that linear body measurement is a reliable classification criterion for discriminating among the phenotypes, which are therefore, correctly described as different breeds/strains. Therefore, for rapid improvement in local turkeys and breed development, these linear body traits could be used to enhance its discrimination, classification and appropriate grouping into breeds. The present findings could assist in the design of long-term genetic improvement programmes for local turkey breeding and production in Nigeria.

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42

Yang, Bo, and Songcan Chen. "A Comparative Study: Globality versus Locality for Graph Construction in Discriminant Analysis." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/965602.

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Localgraph based discriminant analysis (DA) algorithms recently have attracted increasing attention to mitigate the limitations ofglobal(graph) DA algorithms. However, there are few particular concerns on the following important issues: whether the local construction is better than the global one for intraclass and interclass graphs, which (intraclass or interclass) graph should locally or globally be constructed? and, further how they should be effectively jointed for good discriminant performances. In this paper, pursuing our previous studies on the graph construction and DA, we firstly address the issues involved above, and then by jointly utilizing both the globality and the locality, we develop, respectively, a Globally marginal and Locally compact Discriminant Analysis (GmLcDA) algorithm based on so-introduced global interclass and local intraclass graphs and a Locally marginal and Globally compact Discriminant Analysis (LmGcDA) based on so-introduced local interclass and global intraclass graphs, the purpose of which is not to show how novel the algorithms are but to illustrate the analyses in theory. Further, by comprehensively comparing the Locally marginal and Locally compact DA (LmLcDA) based on locality alone, the Globally marginal and Globally compact Discriminant Analysis (GmGcDA) just based on globality alone, GmLcDA, and LmGcDA, we suggest that the joint of locally constructed intraclass and globally constructed interclass graphs is more discriminant.

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SU, Zuqiang. "Fault Diagnosis Method Based on Orthogonal Semi-supervised Local Fisher Discriminant Analysis." Journal of Mechanical Engineering 50, no. 18 (2014): 7. http://dx.doi.org/10.3901/jme.2014.18.007.

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Zhao, Xiaoming, and Shiqing Zhang. "Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap." Sensors 11, no. 10 (October 11, 2011): 9573–88. http://dx.doi.org/10.3390/s111009573.

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Sakarya, U. "Hyperspectral dimension reduction using global and local information based linear discriminant analysis." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-7 (September 19, 2014): 61–66. http://dx.doi.org/10.5194/isprsannals-ii-7-61-2014.

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Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing. In addition, not only tuning the parameters, but also an experimental analysis of the distribution of the hyperspectral data is demonstrated. Therefore, how global or local pattern variations play an important role in classification is examined. According to the experimental outcomes, the promising results are obtained for classification on hyperspectral images.

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Shi, Mingkuan, Rongzhen Zhao, Yaochun Wu, and Tianjing He. "Fault diagnosis of rotor based on Local-Global Balanced Orthogonal Discriminant Projection." Measurement 168 (January 2021): 108320. http://dx.doi.org/10.1016/j.measurement.2020.108320.

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Cheng, Miao, Bin Fang, Yuan Yan Tang, and Jing Wen. "Direct Neighborhood Discriminant Analysis for Face Recognition." Mathematical Problems in Engineering 2008 (2008): 1–15. http://dx.doi.org/10.1155/2008/825215.

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Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.

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Ameen, Azad Abdullah, Hardi M. M-Saleh, and Zrar Kh Abdul. "Wavelet-Local binary pattern based face recognition." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 1 (February 28, 2017): 7552–56. http://dx.doi.org/10.24297/ijct.v16i1.5779.

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Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.

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Liang, Xing Zhu, Jing Zhao Li, and Yu E. Lin. "Kernel Orthogonal Neighborhood Preserving Discriminant Analysis." Advanced Materials Research 339 (September 2011): 571–74. http://dx.doi.org/10.4028/www.scientific.net/amr.339.571.

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Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods still are linear techniques in nature. In this paper, we present nonlinear feature extraction method called Kernel Orthogonal Neighborhood Preserving Discriminant Analysis (KONPDA). A major advantage of the proposed method is that it is regarded every column of the kernel matrix as a corresponding sample. Then running KONPDA in kernel matrix, nonlinear features can be extracted. Experimental results on ORL database indicate that the proposed KONPDA method achieves higher recognition rate than the ONPDA method and other kernel-based learning algorithms.

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Lv, Yuan Xing, Yan Ni Deng, Yuan Shi, Qiang Li, and Wen Peng. "Adaptive Discriminant Linear Local Tangent Space Alignment Algorithm on Face Recognition." Advanced Materials Research 989-994 (July 2014): 2381–84. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2381.

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This paper proposes an adaptive discriminant linear local tangent space alignment algorithm DALLTSA. On the basis of LLTSA algorithm adding adaptive and discriminant gets DALLTSA.DALLTSA not only combines characteristics in DLLTSA that maintain the local geometry and meets the maximum between-class scatter matrix, but also dynamically selects K-neighbor better to reflect the degree of polymerization between samples. Finally, the face recognition experiments based on Gabor [1] filter and DALLTSA shows that this algorithm improves the recognition rate and robustness.

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