Conference Proceeding
A Randomized Approach to Efficient Kernel Clustering 公开 Deposited
https://scholar.colorado.edu/concern/conference_proceedings/jh343t059
- Abstract
- Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods that have more modest memory requirements, and propose a specific one-pass randomized kernel approximation followed by standard Kmeans on the transformed data. The analysis and experiments suggest the method is accurate, while requiring drastically less memory than standard kernel K-means and significantly less memory than Nystr¨om based approximations.
- Creator
- Date Issued
- 2016-08-01
- Conference Name
- Additional Information
- ©IEEE
- Academic Affiliation
- Subject
- 最新修改
- 2020-01-09
- Resource Type
- 权利声明
- Language