The linear subspace model is pervasive in science and engineering and particularly in large datasets which are often incomplete due to missing measurements and privacy issues. Therefore, a critical problem in modeling is to develop algorithms for estimating a low-dimensional subspace model from incomplete data efficiently in terms of both computational complexity and memory storage. In this paper we study an algorithm that processes blocks of incomplete data to estimate the underlying subspace model. Our algorithm has a simple interpretation as optimizing the subspace to fit the observed data block but remain close to the previous estimate. We prove a linear rate of convergence for the algorithm and our rate holds with high probability.
['Armin Eftekhari', 'Laura Balzano', 'Dehui Yang', 'Michael B. Wakin']
SNIPE for Memory-Limited PCA From Incomplete Data