@inproceedings{krishnamurthy2014subspace,
author = {Krishnamurthy, Akshay and Azizyan, Martin and Singh, Aarti},
title = {Subspace learning from extremely compressed measurements},
booktitle = {Asilomar Conference on Signals, Systems, and Computers},
year = {2014},
month = {November},
abstract = {We consider learning the principal subspace of a large set of vectors from an extremely small number of compressive measurements of each vector. Our theoretical results show that even a constant number of measurements per column suffices to approximate the principal subspace to arbitrary precision, provided that the number of vectors is large. This result is achieved by a simple algorithm that computes the eigenvectors of an estimate of the covariance matrix. The main insight is to exploit an averaging effect that arises from applying a different random projection to each vector. We provide a number of simulations confirming our theoretical results.},
url = {http://approjects.co.za/?big=en-us/research/publication/subspace-learning-from-extremely-compressed-measurements/},
}