https://www.selleckchem.com/pr....oducts/gsk2656157.ht
Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen-decomposition solution, it is hard to apply PCA to the large-scale data with high dimensionality, e.g., millions of data points with millions of variables. Meanwhile, the squared L2-norm based objective makes it sensitive to data outliers. In recent research, the L1-norm maximization based PCA method was proposed for efficient computation and being robust
Aimer
Commentaire
Partagez
