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View all- Wicker JHua YRebello RPfahringer B(2019)XOR-Based Boolean Matrix Decomposition2019 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2019.00074(638-647)Online publication date: Nov-2019
Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data into single-node or distributed main memory to ...
Randomized sampling has recently been proven a highly efficient technique for computing approximate factorizations of matrices that have low numerical rank. This paper describes an extension of such techniques to a wider class of matrices that are not ...
We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, or "compression," of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-...
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