Combining Structured and Unstructured Randomness in Large Scale PCA

23 Oct 2013Nikos KarampatziakisPaul Mineiro

Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In this paper, we present an algorithm for computing the top principal components of a dataset with a large number of rows (examples) and columns (features)... (read more)

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