Efficient large-scale kernel learning using a distributed processing architecture
US10997525B2 · kind B2 · utility
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Key dates
| Filing date | Nov 20, 2017 |
| Grant date | May 4, 2021 |
| Priority date | — |
| Expiry date | Feb 17, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/40
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system of creating a model for large scale data analytics is provided. Training data is received in a form of a data matrix X and partitioned into a plurality of partitions. A random matrix T is generated. A feature matrix is determined based on multiplying the partitioned training data by the random matrix T. A predicted data {tilde over (y)} is determined for each partition via a stochastic average gradient (SAG) of each partition. A number of SAG values is reduced based on a number of rows n in the data matrix X. For each iteration, a sum of the reduced SAG values is determined, as well as a full gradient based on the sum of the reduced SAG values from all rows n, by distributed parallel processing. The model parameters w are updated based on the full gradient for each partition.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.