Investigation on PCA in frequency domain
With the development of science and technology, the dimension of dataset has grown to be be higher and higher. To conveniently cope with them, a method called principal component analysis (PCA) has been invented to reduce the dimension while maintain most information of dataset. Nowadays, data in th...
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2022
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sg-ntu-dr.10356-1569322023-02-28T23:14:56Z Investigation on PCA in frequency domain Duan, Zhaoming Pan Guangming School of Physical and Mathematical Sciences GMPAN@ntu.edu.sg Science::Mathematics::Analysis With the development of science and technology, the dimension of dataset has grown to be be higher and higher. To conveniently cope with them, a method called principal component analysis (PCA) has been invented to reduce the dimension while maintain most information of dataset. Nowadays, data in the signal format has shown up in many industry fields. To decompose the multivariate non-stationary signal into components that have zero coherency, PCA in the frequency domain is applied. It is usually applied in Factor Analysis and Signal Analysis. In our study, the dependencies of PCA in the frequency domain were explored. With the factor model and spiked model, the synthetic data was generated. Based on the dataset and PCA in frequency domain, the result of this technique was able to be analyzed. Periodic and symmetric behavior of the PCA result was revealed. We found that the dataset ratio and the type of distribution for data generation would affect the PCA result. This research is valuable for signal processing field, which may help researchers to improve the performance of PCA in the frequency domain in electroencephalogram, fault diagnosis, factor analysis and etc. Bachelor of Science in Mathematical Sciences 2022-04-29T00:08:50Z 2022-04-29T00:08:50Z 2022 Final Year Project (FYP) Duan, Z. (2022). Investigation on PCA in frequency domain. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156932 https://hdl.handle.net/10356/156932 en MATH/21/082 application/pdf Nanyang Technological University |
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Science::Mathematics::Analysis Duan, Zhaoming Investigation on PCA in frequency domain |
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With the development of science and technology, the dimension of dataset has grown to be be higher and higher. To conveniently cope with them, a method called principal component analysis (PCA) has been invented to reduce the dimension while maintain most information of dataset. Nowadays, data in the signal format has shown up in many industry fields. To decompose the multivariate non-stationary signal into components that have zero coherency, PCA in the frequency domain is applied. It is usually applied in Factor Analysis and Signal Analysis. In our study, the dependencies of PCA in the frequency domain were explored. With the factor model and spiked model, the synthetic data was generated. Based on the dataset and PCA in frequency domain, the result of this technique was able to be analyzed. Periodic and symmetric behavior of the PCA result was revealed. We found that the dataset ratio and the type of distribution for data generation would affect the PCA result. This research is valuable for signal processing field, which may help researchers to improve the performance of PCA in the frequency domain in electroencephalogram, fault diagnosis, factor analysis and etc. |
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Pan Guangming |
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Pan Guangming Duan, Zhaoming |
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Final Year Project |
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Duan, Zhaoming |
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Duan, Zhaoming |
title |
Investigation on PCA in frequency domain |
title_short |
Investigation on PCA in frequency domain |
title_full |
Investigation on PCA in frequency domain |
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Investigation on PCA in frequency domain |
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Investigation on PCA in frequency domain |
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investigation on pca in frequency domain |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/156932 |
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