Image Representation Learning Using Graph Regularized Auto-Encoders
Authors: Yiyi Liao, Yue Wang, Yong Liu
ICLR 2014

Abstract: We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning. In those learning tasks, the raw image vectors may not provide enough representation for their intrinsic structures due to their highly dense feature space. To overcome this problem, the raw image vectors should be mapped to a proper representation space which can capture the latent structure of the original data and represent the data explicitly for further learning tasks such as clustering. Inspired by the recent research works on deep neural network and representation learning, in this paper, we introduce the multiple-layer auto-encoder into image representation, we also apply the locally invariant ideal to our image representation with auto-encoders and propose a novel method, called Graph regularized Auto-Encoder (GAE). GAE can provide a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. Extensive experiments on image clustering show encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-word cases.


Peer review:
[Review]:
An algorithm is presented to generate a representation for unsupervised and partially labelled data  with graph-based regularization to keep nearest-neighbors close.
English is not clear in some places. 

I found the whole paper hard work to follow. 

Results tables don't give column headings. It wasn't clear what the variable was - the dimension? 
It would be clearer to break out the 'error measure' into a separate column.

The two metrics used weren't very clear to me -
the mapping from unsupervised k-means clusters to class labels

How does it compare to a simple 1-NN hinge-loss technique similar to those used by Weston e.g. for training joint embeddings? 
http://www.thespermwhale.com/jaseweston/papers/wsabie-ijcai.pdf

What parameters eta, lambda $k$ (for the graph construction) were chosen? How did this $k$ affect the results?

How expensive is the proposed technique compared to the alternative?