A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Minimally Redundant Laplacian Eigenmaps
2018
International Conference on Learning Representations
Spectral algorithms for learning low-dimensional data manifolds have largely been supplanted by deep learning methods in recent years. One reason is that classic spectral manifold learning methods often learn collapsed embeddings that do not fill the embedding space. We show that this is a natural consequence of data where different latent dimensions have dramatically different scaling in observation space. We present a simple extension of Laplacian Eigenmaps to fix this problem based on
dblp:conf/iclr/PfauB18
fatcat:gtwyalwn7rdifdvdxzlvnuwbgi