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Introduction to Principal Component Analysis (PCA) by J.-S. Roger Jang from MIR Lab, CSIE Dept., National Taiwan University. PCA is a method for reducing dataset dimensionality while preserving spatial characteristics. It has applications in line/plane fitting, face recognition, and machine learning

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The presentation provides an update on the status of V*LIDORT and other radiative transfer models as discussed at the Third TEMPO Science Team Meeting. It covers the LIDORT family overview, upgrades to the codes, new releases for RT models, and accelerated RT developments using PCA. The V*LIDORT cod

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In machine learning, Principal Components Analysis (PCA) is a common method for dimensionality reduction. It helps combine information from multiple features into a smaller set, focusing on directions of highest variance to eliminate noise in the data. PCA is unsupervised and works well with linear

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