Unlike PCA (maximum variance) or ICA (maximum independence), ForeCA finds components that are maximally forecastable. This makes it ideal for time series analysis where prediction is often the primary ...
AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...
Abstract: Robust tensor principal component analysis (RTPCA) based on tensor singular value decomposition (t-SVD) separates the low-rank component and the sparse component from the multiway data. For ...
Abstract: The matrix Bingham distribution is a measure on the set of low-dimensional frames that has found applications in mixture models, shape analysis, statistical signal processing, and control.
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