Li, ZongpengZhao, Yao2015-09-242015-11-202015-09-242015http://hdl.handle.net/11023/2488Data inference allows to infer data from a partially revealed data set or to detect corrupted data and recover the original data. In general, to infer missing data or to correct corrupted data is theoretically impossible if given no assumption about the data. Under assumptions about the intrinsic features of the data set, algorithms can be developed to recover missing or corrupted data. We consider data inference problems with a low-rank structure of the data matrix. By exploiting the low-rank feature of the matrix, the data inference problems can be modelled as an L1-norm optimization problem. We propose a framework to solve this kind of problems exploiting optimization theory on Grassmann manifold. We apply this framework to Smart Grid to detect false data injection attack and to predict the QoS of a cloud marketplace. The experiments show our framework achieves a good result under both scenarios.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Computer ScienceGrassmann manifoldData inferenceQoSSmart gridsmaster thesis10.11575/PRISM/25519