Abstract
Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with 'Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for real-time processing pose major challenges towards achieving this goal. In this context, the fresh look advocated here permeates benefits from rank minimization to track low-dimensional subspaces from incomplete data. Leveraging the low-dimensionality of the subspace sought, a novel estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the nuclear norm. After recasting the non-separable nuclear norm into a form amenable to online optimization, a real-time algorithm is developed and its convergence established under simplifying technical assumptions. The novel subspace tracker can asymptotically offer the well-documented performance guarantees of the batch nuclear-norm regularized estimator. Simulated tests with real Internet data confirm the efficacy of the proposed algorithm in tracking the traffic subspace, and its superior performance relative to state-of-the-art alternatives.
Original language | English (US) |
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Title of host publication | 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings |
Pages | 5681-5685 |
Number of pages | 5 |
DOIs | |
State | Published - Oct 18 2013 |
Event | 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada Duration: May 26 2013 → May 31 2013 |
Other
Other | 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 |
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Country/Territory | Canada |
City | Vancouver, BC |
Period | 5/26/13 → 5/31/13 |
Keywords
- Low rank
- matrix completion
- online algorithm