3,297 research outputs found
Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural model
that discovers clusters of data points drawn from a union of low-dimensional
subspaces. In contrast to previous attempts, our model runs without the aid of
spectral clustering. This makes our algorithm one of the kinds that can
gracefully scale to large datasets. At its heart, our neural model benefits
from a classifier which determines whether a pair of points lies on the same
subspace or not. Essential to our model is the construction of two affinity
matrices, one from the classifier and the other from a notion of subspace
self-expressiveness, to supervise training in a collaborative scheme. We
thoroughly assess and contrast the performance of our model against various
state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201
Deep Networks for Compressed Image Sensing
The compressed sensing (CS) theory has been successfully applied to image
compression in the past few years as most image signals are sparse in a certain
domain. Several CS reconstruction models have been recently proposed and
obtained superior performance. However, there still exist two important
challenges within the CS theory. The first one is how to design a sampling
mechanism to achieve an optimal sampling efficiency, and the second one is how
to perform the reconstruction to get the highest quality to achieve an optimal
signal recovery. In this paper, we try to deal with these two problems with a
deep network. First of all, we train a sampling matrix via the network training
instead of using a traditional manually designed one, which is much appropriate
for our deep network based reconstruct process. Then, we propose a deep network
to recover the image, which imitates traditional compressed sensing
reconstruction processes. Experimental results demonstrate that our deep
networks based CS reconstruction method offers a very significant quality
improvement compared against state of the art ones.Comment: This paper has been accepted by the IEEE International Conference on
Multimedia and Expo (ICME) 201
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