SmoothHOOI, a Tucker tensor decomposition with temporal smoothing and missing-data support, recovers circadian patterns and detects an association between OSA severity and overall blood pressure and heart rate levels that summary statistics miss.
Regularized Singular Value Decomposition and Application to Recommender System
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abstract
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.
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Smooth tensor decomposition with application to ambulatory blood pressure monitoring data
SmoothHOOI, a Tucker tensor decomposition with temporal smoothing and missing-data support, recovers circadian patterns and detects an association between OSA severity and overall blood pressure and heart rate levels that summary statistics miss.