Incremental dictionary learning via geometric sparse encoding

Hudes, Matthew I.

2023

Description
  • We consider the incremental learning of sparse representations of high dimensional data, whereby learning occurs continuously from a stream of data. This is in contrast to learning methods that assume all data can be accessed at once. We propose a framework based on a geometric regularizer that encourages sparsity by representing data points via local landmark points (atoms). The sparse representations ... read more
This object is in collection Creator department Thesis Type Subject Genre Permanent URL
ID:
m900p8808
To Cite:
TARC Citation Guide    EndNote
Usage:
Detailed Rights