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MQ-KPCA: Custom Kernel PCA for Classification of Microscopic Images
https://orcid.org/http://orcid.org/0000-0003-0668-926X
Principal component analysis (PCA) is an efficient and linear feature transformation technique. However, it limited to linear components, because it defined on the mean and covariance matrix’s eigenvectors of the data. This paper proposes a new modified method of kernel PCA (KPCA) using a Multiquadric function, called MQ-KPCA. This is more efficient for classification, compared to other KPCA techniques and performs more nonlinear at dimension reduction with keeping variation of data. The proposed dimension transformation technique tested with texture features extracted from defective steel surfaces collected from the Northeastern University dataset and cancer-affected lymph nodes and Chinese hamster ovary cells gathered from IICBU Biological Image Repository, and achieved better results compared to other KPCA methods.
MQ-KPCA: Custom Kernel PCA for Classification of Microscopic Images
https://orcid.org/http://orcid.org/0000-0003-0668-926X
Principal component analysis (PCA) is an efficient and linear feature transformation technique. However, it limited to linear components, because it defined on the mean and covariance matrix’s eigenvectors of the data. This paper proposes a new modified method of kernel PCA (KPCA) using a Multiquadric function, called MQ-KPCA. This is more efficient for classification, compared to other KPCA techniques and performs more nonlinear at dimension reduction with keeping variation of data. The proposed dimension transformation technique tested with texture features extracted from defective steel surfaces collected from the Northeastern University dataset and cancer-affected lymph nodes and Chinese hamster ovary cells gathered from IICBU Biological Image Repository, and achieved better results compared to other KPCA methods.
MQ-KPCA: Custom Kernel PCA for Classification of Microscopic Images
https://orcid.org/http://orcid.org/0000-0003-0668-926X
Principal component analysis (PCA) is an efficient and linear feature transformation technique. However, it limited to linear components, because it defined on the mean and covariance matrix’s eigenvectors of the data. This paper proposes a new modified method of kernel PCA (KPCA) using a Multiquadric function, called MQ-KPCA. This is more efficient for classification, compared to other KPCA techniques and performs more nonlinear at dimension reduction with keeping variation of data. The proposed dimension transformation technique tested with texture features extracted from defective steel surfaces collected from the Northeastern University dataset and cancer-affected lymph nodes and Chinese hamster ovary cells gathered from IICBU Biological Image Repository, and achieved better results compared to other KPCA methods.
MQ-KPCA: Custom Kernel PCA for Classification of Microscopic Images
J. Inst. Eng. India Ser. B
Suresha, M. (author) / Raghukumar, D. S. (author) / Kuppa, S. (author) / Raghavendra, R. S. (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 2025-2033
2022-12-01
9 pages
Article (Journal)
Electronic Resource
English
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