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SVD factorizes the matrix M as a product of 3 matrices:
where U and V are orthogonal matrices of size n × n and S is a n × n diagonal matrix with diagonal values sorted from high to low. The rank k (k
Where Uk is a n × k matrix, Sk is a k × k diagonal matrix and Vk T is a k × n matrix. Uk Sk is the matrix of size n × k, which represents the n concepts in k dimensions. We set k=300 and used Python scikit-learn library to implement truncated SVD and obtain the 300 D concept embedding [38].
J Med Internet Res 2022;24(8):e39888
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