On the sample complexity of subspace clustering with missing data

Subspace clustering is a useful tool for analyzing large complex data, but in many relevant applications missing data are common. Existing theoretical analysis of this problem shows that subspace clustering from incomplete data is possible, but that analysis requires the number of samples (i.e., partially observed vectors) to be super-polynomial in the dimension d. Such huge sample sizes are unnecessary when no data are missing and uncommon in applications. There are two main contributions in this paper. First, it is shown that if subspaces have rank at most r and the number of partially observed vectors greater than d
^r+1
(times a poly-logarithmic factor), then with high probability the true subspaces are the only subspaces that agree with the observed data. We may conclude that subspace clustering may be possible without impractically large sample sizes and that we can certify the output of any subspace clustering algorithm by checking its fit to the observed data. The second main contribution is a novel EM-type algorithm for subspace clustering with missing data. We demonstrate and compare it to several other algorithms. Experiments with simulated and real data show that such algorithms work well in practice.