![]() A special class of matrices, referred to as low-rank (high-dimensional) matrices, which often have many linearly dependent rows (or columns), is often encountered when various big data analytics applications need to be addressed. For social networks, matrices such as adjacency and Laplacian matrices have been used to encode social–graph relations . For example, a bicycle demand–supply problem was formulated as a matrix-completion problem by modeling the bike-usage demand as a matrix whose two dimensions were defined as the time interval of a day and the region of a city . For example, matrix factorization techniques have been applied for topic modeling and text mining . In big-data analysis, matrices are utilized extensively in formulating problems with linear structure . ![]()
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