Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis
نویسندگان
چکیده
منابع مشابه
Robust Principal Component Analysis by Projection Pursuit
Different algorithms for principal component analysis (PCA) based on the idea of projection pursuit are proposed. We show how the algorithms are constructed, and compare the new algorithms with standard algorithms. With the R implementation pcaPP we demonstrate the usefulness at real data examples. Finally, it will be outlined how the algorithms can be used for robustifying other multivariate m...
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A remarkably simple, yet powerful, algorithm termed Coherence Pursuit for robust Principal Component Analysis (PCA) is presented. In the proposed approach, an outlier is set apart from an inlier by comparing their coherence with the rest of the data points. As inliers lie in a low dimensional subspace, they are likely to have strong mutual coherence provided there are enough inliers. By contras...
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Principal Component Analysis (PCA) is very sensitive in presence of outliers. One of the most appealing robust methods for principal component analysis uses the Projection-Pursuit principle. Here, one projects the data on a lower-dimensional space such that a robust measure of variance of the projected data will be maximized. The Projection-Pursuit based method for principal component analysis ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2017
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2017.2749215