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Principal Component Analysis (PCA) is a technique to transform the original set of variables into a smaller set of linear combinations that account for most of the original set variance. The data reduction based on the classical PCA is fruitless if outlier is present in the data. The decomposed classical covariance matrix is very sensitive to outlying observations. ROBPCA is an effective PCA method...
Outlier labeling can be considered as an early procedure to get the information of ‘suspects’. This paper introducesrobust kurtosis projection algorithm for multivariate outlier labeling of data set with moderate, high and very high percentage outlier. The algorithm works in two stages. In the first stage, we propose a projection approach to findthe orthonormal set of all vectors that maximize the...
The robust dimension reduction for classification of two dimensional data is discussed in this paper. The classification process is done with reference of original data. The classifying of class membership is not easy when more than one variable are loaded with the same information, and they can be written as a near linear combination of other variables. The standard approach to overcome this problem...
This paper discusses the classic and robust discriminant analysis algorithm applied to the classification of rice fields, water, buildings, and bare land areas. Discriminant Analysis for multiple groups is often done. This method relies on the sample averages and covariance matrices computed from the training sample. Since sample averages and covariance matrices are not robust, it has been proposed...
This paper discusses an efficient and effective robust algorithm applied to the classification of vegetation areas in the Jakarta Province. The input data is remote sensing data from the Landsat 7 Satellite. The classification process is guided over two steps, training and classification. The purpose of the training step is to determine the reference spectra of the vegetation area, and the purpose...
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