Comparison of principal component analysis and linear discriminant analysis for face recognition (March 2007)
- Robinson, P. E., Clarke, W. A.
- Authors: Robinson, P. E. , Clarke, W. A.
- Date: 2007
- Subjects: Face recognition , Eigenfaces , Fisherfaces , Principal component analysis , Linear discriminant analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/16300 , uj:15759 , Robinson, P.E. & Clarke, W.A. Comparison of Principal Component Analysis and Linear Discriminant Analysis for face recognition (March 2007), in AFRICON 2007:1-6
- Description: Abstract: In this paper two Face Recognition techniques, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), are considered and implemented using a Nearest Neighbor classifier. The performance of the two techniques is then compared in facial recognition and detection tasks. The comparisons are done using a facial recognition database captured for the project that contains images captured over a range of poses, lighting conditions and occlusions.
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- Authors: Robinson, P. E. , Clarke, W. A.
- Date: 2007
- Subjects: Face recognition , Eigenfaces , Fisherfaces , Principal component analysis , Linear discriminant analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/16300 , uj:15759 , Robinson, P.E. & Clarke, W.A. Comparison of Principal Component Analysis and Linear Discriminant Analysis for face recognition (March 2007), in AFRICON 2007:1-6
- Description: Abstract: In this paper two Face Recognition techniques, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), are considered and implemented using a Nearest Neighbor classifier. The performance of the two techniques is then compared in facial recognition and detection tasks. The comparisons are done using a facial recognition database captured for the project that contains images captured over a range of poses, lighting conditions and occlusions.
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Analysing factors influencing e-government development in Zambia : a principal component analysis approach
- Authors: Bwalya, Kelvin Joseph
- Date: 2017
- Subjects: Adoption , e-Government , Principal component analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/259405 , uj:27299 , Citation: Bwalya, K.J. 2017. Analysing factors influencing e-government development in Zambia : a principal component analysis approach.
- Description: Abstract: Effervescent e-Government development entails that e-Government applications and solutions are accessed by a majority of citizens and businesses accessing many of government information or services and participating in the different governance value chains. In the case of Zambian where e-Government development is in its nascent stages, anecdotal evidence suggests that a majority of the population and businesses do not engage in e-Government let alone know that it is being implemented in Zambia. Because of a large number of e-Government projects failing to meet their expectations especially in resource-constrained environments, the need to carefully understand contextual factors influencing e-Government development cannot be overemphasized. This research explores multivariate analysis of factors modelled as multivariate random variables. The study analyses individual factors influencing e-Government using Principal Component Analysis (PCA) as a factor reduction process. The end of PCA shows the critical factors at the centre of e-Government development in Zambia.
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- Authors: Bwalya, Kelvin Joseph
- Date: 2017
- Subjects: Adoption , e-Government , Principal component analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/259405 , uj:27299 , Citation: Bwalya, K.J. 2017. Analysing factors influencing e-government development in Zambia : a principal component analysis approach.
- Description: Abstract: Effervescent e-Government development entails that e-Government applications and solutions are accessed by a majority of citizens and businesses accessing many of government information or services and participating in the different governance value chains. In the case of Zambian where e-Government development is in its nascent stages, anecdotal evidence suggests that a majority of the population and businesses do not engage in e-Government let alone know that it is being implemented in Zambia. Because of a large number of e-Government projects failing to meet their expectations especially in resource-constrained environments, the need to carefully understand contextual factors influencing e-Government development cannot be overemphasized. This research explores multivariate analysis of factors modelled as multivariate random variables. The study analyses individual factors influencing e-Government using Principal Component Analysis (PCA) as a factor reduction process. The end of PCA shows the critical factors at the centre of e-Government development in Zambia.
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Characterising cycles exhibited by important financial sections in the South African economy
- De Wet, Milan C., Botha, Ilse
- Authors: De Wet, Milan C. , Botha, Ilse
- Date: 2019
- Subjects: Financial cycles , Spectral density analysis , Principal component analysis
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/293137 , uj:31868 , Citation: De Wet, M.C. & Botha, I., 2019, ‘Characterising cycles exhibited by important financial sections in the South African economy’, Journal of Economic and Financial Sciences 12(1), a433. https://doi.org/ 10.4102/jef.v12i1.433 , ISSN: 2312-2803 (Online) , ISSN: 1995-7076 (Print)
- Description: Abstract: Orientation: The 2007–2008 global financial crisis caused negative spillovers to the real economy of the United States as well as other economies across the world. Research purpose: The main aim of this article is to determine the cyclical characteristics of important South African financial sections. Motivation for the study: Financial cycles are complex, making them hard to measure and understand. This, in turn, makes financial cycles and the effect of fluctuations in financial cycles hard to predict and manage...
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- Authors: De Wet, Milan C. , Botha, Ilse
- Date: 2019
- Subjects: Financial cycles , Spectral density analysis , Principal component analysis
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/293137 , uj:31868 , Citation: De Wet, M.C. & Botha, I., 2019, ‘Characterising cycles exhibited by important financial sections in the South African economy’, Journal of Economic and Financial Sciences 12(1), a433. https://doi.org/ 10.4102/jef.v12i1.433 , ISSN: 2312-2803 (Online) , ISSN: 1995-7076 (Print)
- Description: Abstract: Orientation: The 2007–2008 global financial crisis caused negative spillovers to the real economy of the United States as well as other economies across the world. Research purpose: The main aim of this article is to determine the cyclical characteristics of important South African financial sections. Motivation for the study: Financial cycles are complex, making them hard to measure and understand. This, in turn, makes financial cycles and the effect of fluctuations in financial cycles hard to predict and manage...
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Exploring the effects of compression via principal components analysis on X-ray image classification
- Rameshar, Vikash, Doorsamy, Wesley
- Authors: Rameshar, Vikash , Doorsamy, Wesley
- Date: 2019
- Subjects: X-ray , Image classification , Principal component analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/405805 , uj:34092 , Citation: Rameshar, V. & Doorsamy, W. 2019. Exploring the effects of compression via principal components analysis on X-ray image classification.
- Description: Abstract: Image compression in medical applications implores careful consideration of the effects on data veracity. The inexorable challenge of assessing the volume-veracity trade-off is becoming more prevalent in this critical application area, and particularly when machine learning is used for the purpose of assisted diagnostics. This paper investigates the impact of compressing X-ray images on the accuracy of fracture diagnostics. The accuracy of the classification system is assessed for X-ray images of both healthy and fracture bones when subjected to different levels of compression. Compression is achieved using principal components analysis. Results indicate that accuracy is only marginally affected under a level one compression but begins to deteriorate under level two compression. These results are potentially useful as the level one compression yields gains up to 94% with less than a 2% drop in classification accuracy.
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Exploring the effects of compression via principal components analysis on X-ray image classification
- Authors: Rameshar, Vikash , Doorsamy, Wesley
- Date: 2019
- Subjects: X-ray , Image classification , Principal component analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/405805 , uj:34092 , Citation: Rameshar, V. & Doorsamy, W. 2019. Exploring the effects of compression via principal components analysis on X-ray image classification.
- Description: Abstract: Image compression in medical applications implores careful consideration of the effects on data veracity. The inexorable challenge of assessing the volume-veracity trade-off is becoming more prevalent in this critical application area, and particularly when machine learning is used for the purpose of assisted diagnostics. This paper investigates the impact of compressing X-ray images on the accuracy of fracture diagnostics. The accuracy of the classification system is assessed for X-ray images of both healthy and fracture bones when subjected to different levels of compression. Compression is achieved using principal components analysis. Results indicate that accuracy is only marginally affected under a level one compression but begins to deteriorate under level two compression. These results are potentially useful as the level one compression yields gains up to 94% with less than a 2% drop in classification accuracy.
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Experimental application of self-organizing feature maps and principal component analysis for generator condition assessment
- Swana, Elsie Fezeka, Doorsamy, Wesley, Bokoro, Pitshou
- Authors: Swana, Elsie Fezeka , Doorsamy, Wesley , Bokoro, Pitshou
- Date: 2020
- Subjects: Unsupervised learning , Self-organizing feature map , Principal component analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/453231 , uj:40006 , Citation: Swana, E.F., Doorsamy, W. & Bokoro, P. 2020. Experimental application of self-organizing feature maps and principal component analysis for generator condition assessment.
- Description: Abstract: Data-driven approaches are gaining interest in the area of condition monitoring in electrical machines, because of the increasing availability of condition data – including the ability to capture such data – as well as the added flexibility over more traditional model-based approaches. Despite these benefits of data-driven approaches, the challenges of data imbalance, that is, the lack of availability of fault data as opposed to healthy operation data brings into question the suitability and practicability of deployment these methods. Thus, within the sphere of data-driven approaches, unsupervised learning or exploratory techniques could potentially lead to overcoming such challenges. This paper presents an experimental investigation into the use of self-organizing feature maps and principal component analysis for application in condition monitoring on a wound-rotor induction generator. A comparative analysis of these two techniques is conducted to determine the suitability thereof for condition assessment of the experimental generator under four different incipient fault cases. Both techniques are applied on experimentally measured voltage and current features. Results indicate that the self-organizing feature map technique do not yield suitable separation of condition clusters, whereas principal component analysis provides notably better performance.
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- Authors: Swana, Elsie Fezeka , Doorsamy, Wesley , Bokoro, Pitshou
- Date: 2020
- Subjects: Unsupervised learning , Self-organizing feature map , Principal component analysis
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/453231 , uj:40006 , Citation: Swana, E.F., Doorsamy, W. & Bokoro, P. 2020. Experimental application of self-organizing feature maps and principal component analysis for generator condition assessment.
- Description: Abstract: Data-driven approaches are gaining interest in the area of condition monitoring in electrical machines, because of the increasing availability of condition data – including the ability to capture such data – as well as the added flexibility over more traditional model-based approaches. Despite these benefits of data-driven approaches, the challenges of data imbalance, that is, the lack of availability of fault data as opposed to healthy operation data brings into question the suitability and practicability of deployment these methods. Thus, within the sphere of data-driven approaches, unsupervised learning or exploratory techniques could potentially lead to overcoming such challenges. This paper presents an experimental investigation into the use of self-organizing feature maps and principal component analysis for application in condition monitoring on a wound-rotor induction generator. A comparative analysis of these two techniques is conducted to determine the suitability thereof for condition assessment of the experimental generator under four different incipient fault cases. Both techniques are applied on experimentally measured voltage and current features. Results indicate that the self-organizing feature map technique do not yield suitable separation of condition clusters, whereas principal component analysis provides notably better performance.
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Analysis of convergence in transport infrastructure: a global evidence
- Saba, Charles Shaaba, Ngepah, Nicholas, Odhiambo, Nicholas Mbaya
- Authors: Saba, Charles Shaaba , Ngepah, Nicholas , Odhiambo, Nicholas Mbaya
- Date: 2021
- Subjects: Transportation convergence , Transition patterns , Principal component analysis
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/483268 , uj:43854 , Citation: Saba, C. S., Ngepah, N., & Odhiambo, N. M. (2021). Analysis of convergence in transport infrastructure: a global evidence. European Journal of Transport and Infrastructure Research, 21(2), 137–160. https://doi.org/10.18757/ejtir.2021.21.2.5368
- Description: Abstract: This study investigates the convergence in transport infrastructure for 102 countries spanning 1990-2018 using Phillips and Sul econometric methodology. We constructed a transportation infrastructure by a composite index of transportation computed via principal component analysis (PCA). Our findings suggest the presence of panel convergence at the global level, while the income groups exhibited panel divergence. The results obtained from the iterative testing procedure suggest that the sub-groups exhibited convergence and divergence features. Overall, this study finds that the process of convergence in transportation reflects the desirable emanations of transportation policies sharing possible similar characteristics, at least to some extent, across the globe.
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- Authors: Saba, Charles Shaaba , Ngepah, Nicholas , Odhiambo, Nicholas Mbaya
- Date: 2021
- Subjects: Transportation convergence , Transition patterns , Principal component analysis
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/483268 , uj:43854 , Citation: Saba, C. S., Ngepah, N., & Odhiambo, N. M. (2021). Analysis of convergence in transport infrastructure: a global evidence. European Journal of Transport and Infrastructure Research, 21(2), 137–160. https://doi.org/10.18757/ejtir.2021.21.2.5368
- Description: Abstract: This study investigates the convergence in transport infrastructure for 102 countries spanning 1990-2018 using Phillips and Sul econometric methodology. We constructed a transportation infrastructure by a composite index of transportation computed via principal component analysis (PCA). Our findings suggest the presence of panel convergence at the global level, while the income groups exhibited panel divergence. The results obtained from the iterative testing procedure suggest that the sub-groups exhibited convergence and divergence features. Overall, this study finds that the process of convergence in transportation reflects the desirable emanations of transportation policies sharing possible similar characteristics, at least to some extent, across the globe.
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