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Principal components analysis allows to reduce the dimensionality of a dataset in which there are large number of interelated variabels, while retaining as much as posible of the variation present in dataset. This dimension reduction is achieved by transforming to a new set of variabels that have ne...
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| 格式: | Article NonPeerReviewed |
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[Yogyakarta] : Universitas Gadjah Mada
2001
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| 在線閱讀: | https://repository.ugm.ac.id/18473/ http://i-lib.ugm.ac.id/jurnal/download.php?dataId=1262 |
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| 總結: | Principal components analysis allows to reduce the dimensionality of a dataset in which there are large number of interelated variabels, while retaining as much as posible of the variation present in dataset. This dimension reduction is achieved by transforming to a new set of variabels that have new meaning, called principal components, which are highly uncorrelated and which are ordered so that the first few retain most of the variation present in all of the original variabels. The single layer feedforward linear network with the generalized Hebbian learning algorithm performs principal components analysis of grey-level image that calculate eigenvalue and eigenvector of the correlation matrix of the input data. The original image with dimension 256x256 pixels divide onto small block process with dimension 8x8 then each of the block process is converted to 64x1 input vector. |
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