Abstract
We provide a unified optimization view of iterative Hessian sketch (IHS) and iterative dual random projection (IDRP). We establish a primal-dual connection between the Hessian sketch and dual random projection, and show that their iterative extensions are optimization processes with preconditioning. We develop accelerated versions of IHS and IDRP based on this insight together with conjugate gradient descent, and propose a primal-dual sketch method that simultaneously reduces the sample size and dimensionality.
Original language | English (US) |
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State | Published - Jan 1 2017 |
Externally published | Yes |
Event | 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States Duration: Apr 20 2017 → Apr 22 2017 |
Conference
Conference | 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 |
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Country/Territory | United States |
City | Fort Lauderdale |
Period | 4/20/17 → 4/22/17 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Statistics and Probability