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Generalized canonical correlation analysis (GCCA) aims at extracting common structure from multiple 'views', i.e., high-dimensional matrices representing the same objects in different feature domains – an extension of classical two-view CCA. Existing (G)CCA algorithms have serious scalability issues, since they involve square root factorization of the correlation matrices of the views. The memory...
The K-means clustering problem seeks to partition the columns of a data matrix in subsets, such that columns in the same subset are ‘close’ to each other. The co-clustering problem seeks to simultaneously partition the rows and columns of a matrix to produce ‘coherent’ groups called co-clusters. Co-clustering has recently found numerous applications in diverse areas. The concept readily generalizes...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.