BMF: Block matrix approach to factorization of large scale data
This addresses a bottleneck in handling large datasets for researchers and practitioners in data analysis, though it appears incremental as it builds on existing factorization methods.
The paper tackles the computational and memory challenges of matrix factorization on large-scale data by introducing a block matrix approach, enabling factorization when data exceeds CPU/GPU memory limits.
Matrix Factorization (MF) on large scale matrices is computationally as well as memory intensive task. Alternative convergence techniques are needed when the size of the input matrix is higher than the available memory on a Central Processing Unit (CPU) and Graphical Processing Unit (GPU). While alternating least squares (ALS) convergence on CPU could take forever, loading all the required matrices on to GPU memory may not be possible when the dimensions are significantly higher. Hence we introduce a novel technique that is based on considering the entire data into a block matrix and relies on factorization at a block level.