Learning with group invariant features: A Kernel perspective
We analyze in this paper a random feature map based on a theory of invariance (I-theory) introduced in [1]. More specifically, a group invariant signal signature is obtained through cumulative distributions of group-transformed random projections. Our analysis bridges invariant feature learning with...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Association for Computing Machinery
2017
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| Online Access: | http://hdl.handle.net/1721.1/112309 https://orcid.org/0000-0002-3944-0455 https://orcid.org/0000-0002-5727-9941 |