Accelerating Machine Learning Algorithms with Adaptive Sampling
This addresses efficiency issues for practitioners dealing with big data, but appears incremental as it builds on existing sampling techniques.
The paper tackles the problem of computationally intensive machine learning algorithms on large datasets by substituting subroutines with randomized counterparts, resulting in almost no degradation in quality.
The era of huge data necessitates highly efficient machine learning algorithms. Many common machine learning algorithms, however, rely on computationally intensive subroutines that are prohibitively expensive on large datasets. Oftentimes, existing techniques subsample the data or use other methods to improve computational efficiency, at the expense of incurring some approximation error. This thesis demonstrates that it is often sufficient, instead, to substitute computationally intensive subroutines with a special kind of randomized counterparts that results in almost no degradation in quality.