Relevant bibliographies by topics / Local Discriminant Bases

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Local Discriminant Bases'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Local Discriminant Bases"

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Local Discriminant Bases"

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Tekinay, Cagri. "Classification Of Remotely Sensed Data By Using 2d Local Discriminant Bases." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610782/index.pdf.

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In this thesis, 2D Local Discriminant Bases (LDB) algorithm is used to 2D search structure to classify remotely sensed data. 2D Linear Discriminant Analysis (LDA) method is converted into an M-ary classifier by combining majority voting principle and linear distance parameters. The feature extraction algorithm extracts the relevant features by removing the irrelevant ones and/or combining the ones which do not represent supplemental information on their own. The algorithm is implemented on a remotely sensed airborne data set from Tippecanoe County, Indiana to evaluate its performance. The spectral and spatial-frequency features are extracted from the multispectral data and used for classifying vegetative species like corn, soybeans, red clover, wheat and oat in the data set.
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Kalkan, Habil. "Feature Extraction From Acoustic And Hyperspectral Data By 2d Local Discriminant Bases Search." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610192/index.pdf.

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In this thesis, a feature extraction algorithm based on 2D Local Discriminant Bases (LDB) search is developed for acoustic and hyperspectral data. The developed algorithm extracts the relevant features by both eliminating the irrelevant ones and/or by merging the ones that do not provide extra information on their own. It is implemented on real world data to separate aflatoxin contaminated or high risk hazelnuts from the sound ones by using impact acoustic and hyperspectral data. Impact acoustics data is used to sort cracked and intact shell hazelnuts with high classification accuracy. Hypespectral images of the shelled and roasted (SRT) hazelnuts are used for classification and the algorithm extracted the spectral and spatial-frequency features for that classification. Aflatoxin concentration of the SRT category hazelnuts is automatically decreased to 0.7 ppb from 608 ppb by eliminating the detected contaminated ones.
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Beriat, Pelin. "Non-destructive Testing Of Textured Foods By Machine Vision." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610405/index.pdf.

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In this thesis, two different approaches are used to extract the relevant features for classifying the aflatoxin contaminated and uncontaminated scaled chili pepper samples: Statistical approach and Local Discriminant Bases (LDB) approach. In the statistical approach, First Order Statistical (FOS) features and Gray Level Cooccurrence Matrix (GLCM) features are extracted. In the LDB approach, the original LDB algorithm is modified to perform 2D searches to extract the most discriminative features from the hyperspectral images by removing irrelevant features and/or combining the features that do not provide sufficient discriminative information on their own. The classification is performed by using Linear Discriminant Analysis (LDA) classifier. Hyperspectral images of scaled chili peppers purchased from various locations in Turkey are used in this study. Correct classification accuracy about 80% is obtained by using the extracted features.
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TAKEDA, Kazuya, Norihide KITAOKA, and Makoto SAKAI. "Acoustic Feature Transformation Based on Discriminant Analysis Preserving Local Structure for Speech Recognition." Institute of Electronics, Information and Communication Engineers, 2010. http://hdl.handle.net/2237/14969.

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He, Qiangsen. "Person Re-identification Based on Kernel Local Fisher Discriminant Analysis and Mahalanobis Distance Learning." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36044.

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Person re-identification (Re-ID) has become an intense research area in recent years. The main goal of this topic is to recognize and match individuals over time at the same or different locations. This task is challenging due to the variation of illumination, viewpoints, pedestrians’ appearance and partial occlusion. Previous works mainly focus on finding robust features and metric learning. Many metric learning methods convert the Re-ID problem to a matrix decomposition problem by Fisher discriminant analysis (FDA). Mahalanobis distance metric learning is a popular method to measure similarity; however, since directly extracted descriptors usually have high dimensionality, it’s intractable to learn a high-dimensional semi-positive definite (SPD) matrix. Dimensionality reduction is used to project high-dimensional descriptors to a lower-dimensional space while preserving those discriminative information. In this paper, the kernel Fisher discriminant analysis (KLFDA) [38] is used to reduce dimensionality given that kernelization method can greatly improve Re-ID performance for nonlinearity. Inspired by [47], an SPD matrix is then learned on lower-dimensional descriptors based on the limitation that the maximum intraclass distance is at least one unit smaller than the minimum interclass distance. This method is proved to have excellent performance compared with other advanced metric learning.
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Book chapters on the topic "Local Discriminant Bases"

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Gao, Robert X., and Ruqiang Yan. "Local Discriminant Bases for Signal Classification." In Wavelets, 149–63. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1545-0_9.

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Kalkan, Habil, Çagri Tekinay, and Yasemin Yardimci. "Classification of Multispectral Satellite Land Cover Data by 3D Local Discriminant Bases Algorithm." In Lecture Notes in Electrical Engineering, 237–40. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9794-1_46.

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Marchand, Bradley, and Naoki Saito. "Earth Mover’s Distance-Based Local Discriminant Basis." In Multiscale Signal Analysis and Modeling, 275–94. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4145-8_12.

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Zhang, Lei, Fengchun Tian, and David Zhang. "Local Kernel Discriminant Analysis-Based Odor Recognition." In Electronic Nose: Algorithmic Challenges, 95–113. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2167-2_7.

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Dornaika, Fadi, and Alireza Bosaghzadeh. "Two Subspace-Based Kernel Local Discriminant Embedding." In Artificial Neural Networks and Machine Learning – ICANN 2014, 595–602. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_75.

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Niu, Yanmin, and Xuchu Wang. "Face Recognition Using Null Space-Based Local Discriminant Embedding." In Lecture Notes in Computer Science, 245–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_32.

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Pan, Jeng-Shyang, Shu-Chuan Chu, and Lijun Yan. "Feature Line-Based Local Discriminant Analysis for Image Feature Extraction." In Advances in Intelligent Systems and Computing, 471–78. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07773-4_46.

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Cui, Yan, Chun-Hou Zheng, and Jian Yang. "Dimensionality Reduction for Microarray Data Using Local Mean Based Discriminant Analysis." In Intelligent Computing Theories and Technology, 267–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39482-9_31.

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Pan, Xin, Xuanhe Zhao, Weihong Yan, Jiangping Liu, Xiaoling Luo, and Tana Wuyun. "Identification of Gramineous Grass Seeds Using Local Similarity Pattern and Linear Discriminant Analysis." In Computer Vision based Identification and Mosaic of Gramineous Grass Seeds, 49–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3501-4_5.

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Peterson, Victoria, Rubén Acevedo, Hugo Leonardo Rufiner, and Rubén Spies. "Local Discriminant Wavelet Packet Basis for Signal Classification in Brain Computer Interface." In VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, 584–87. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13117-7_149.

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Conference papers on the topic "Local Discriminant Bases"

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Saito, Naoki, and Ronald R. Coifman. "Local discriminant bases." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1994. http://dx.doi.org/10.1117/12.188763.

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Spooner, C. M. "Application of local discriminant bases to HRR-based ATR." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.987658.

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Kronquist, Goran, and Henrik Storm. "Target detection with local discriminant bases and wavelets." In AeroSense '99, edited by Abinash C. Dubey, James F. Harvey, J. Thomas Broach, and Regina E. Dugan. SPIE, 1999. http://dx.doi.org/10.1117/12.357089.

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Zhang, Di, and Minghui Du. "Diagnosis of prosthetic heart valve using locality preserving kernel fisher discriminant analysis and local discriminant bases." In 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2015. http://dx.doi.org/10.1109/bmei.2015.7401496.

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Jiming, Zheng, Wei Guohua, and Yang Chunde. "Modified Local Discriminant Bases and Its Application in Audio Feature Extraction." In 2009 International Forum on Information Technology and Applications (IFITA). IEEE, 2009. http://dx.doi.org/10.1109/ifita.2009.271.

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Cassabaum, Mary L., Harry A. Schmitt, Hai-Wen Chen, and Jack G. Riddle. "Application of local discriminant bases discrimination algorithm for theater missile defense." In International Symposium on Optical Science and Technology, edited by Akram Aldroubi, Andrew F. Laine, and Michael A. Unser. SPIE, 2000. http://dx.doi.org/10.1117/12.408572.

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Su, Feng, Li Yang, Tong Lu, and Gongyou Wang. "Environmental sound classification for scene recognition using local discriminant bases and HMM." In the 19th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2072298.2072022.

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Pitton, James W., Alan Q. Li, and James Luby. "Automatic classification of mines using local discriminant bases for broadband sonar data." In Aerospace/Defense Sensing and Controls, edited by Abinash C. Dubey, James F. Harvey, and J. Thomas Broach. SPIE, 1998. http://dx.doi.org/10.1117/12.324205.

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Soria, J. A., A. Lombarte, and V. Parisi. "Local discriminant bases representation and non-linear growth processing for species classification and age estimation of fish based on otolith images." In OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean. IEEE, 2008. http://dx.doi.org/10.1109/oceanskobe.2008.4530950.

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Hazaveh, K., and K. Raahemifar. "Optimized local discriminant basis algorithm." In 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698). IEEE, 2003. http://dx.doi.org/10.1109/icme.2003.1221051.

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Reports on the topic "Local Discriminant Bases"

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Barreix, Alberto, and Fernando Velayos. Incentivos tributarios, compromisos internacionales y suficiencia recaudatoria: Otra trilogía imposible. Inter-American Development Bank, April 2021. http://dx.doi.org/10.18235/0003231.

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Los países de América Latina y el Caribe (ALC) utilizan la reducción de la tasa de impuesto a la renta empresarial (IRE) para promover la inversión y, a su vez, este incentivo debe cumplir con la condición de no discriminar entre operaciones de exportación y aquellas destinadas a todo el mercado local, según los compromisos contraídos con la de la Organización Mundial del Comercio (OMC) y el proyecto de Erosión de la Base Imponible y Traslado de Beneficios (BEPS, por sus siglas en inglés). Así se posibilita un importante arbitraje tributario cuando las empresas con incentivos en la tasa de renta empresarial venden con sobreprecios a firmas sujetas a tasa normal en el mercado local, trasvasando rentas a las firmas beneficiadas desde las del régimen general. Esto tiene impactos negativos significativos y crecientes en términos de recaudación y equidad entre firmas bonificadas y las del régimen general en el mismo ramo, especialmente en los servicios cuyo crecimiento es notorio (como los digitales o de telecomunicación). Para morigerar este arbitraje será necesario aplicar algunas opciones prácticas, como las que se presentan más adelante, que hagan viable la bonificación tributaria en la tasa del impuesto como herramienta de políticas, sean compatibles con dichos acuerdos internacionales y reduzcan las pérdidas de recaudación. Adicionalmente, se incluyen cuadros con el resumen de los principales regímenes de incentivos tributarios y de la revisión por los pares de los posibles regímenes fiscales perniciosos en América Latina (Acción 5 de BEPS).
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