#### Transcript SIAM_LowRank

Recovering low rank and sparse matrices from compressive measurements Aswin C Sankaranarayanan Rice University Richard G. Baraniuk Andrew E. Waters Background subtraction in surveillance videos static camera with foreground objects rank 1 background sparse foreground More complex scenarios Changing illumination + foreground motion More complex scenarios Changing illumination + foreground motion Set of all images of a convex Lambertian scene under changing illumination is very close to a 9-dimensional subspace [Basri and Jacobs, 2003] More complex scenarios Changing illumination + foreground motion Video can be represented as a sum of a rank-9 matrix and a sparse matrix Can we use such low rank+sparse model in a compressive recovery framework ? Hyperspectral cube 450nm 490nm 550nm 580nm 720nm Rank approximately equal number of materials in the scene Data courtesy Ayan Chakrabarti, http://vision.seas.harvard.edu/hyperspec/ Robust matrix completion low rank matrix with missing entries low rank matrix Robust matrix completion missing + corrupted entries low rank matrix sparse corruptions Problem formulation • Noisy compressive measurements L: r-rank matrix S: k-sparse matrix • Measurement operator is different for different problems – Video CS: operates on each column of the matrix individually – Matrix completion: sampling operator – Hyperspectral Problem formulation • Noisy compressive measurements L: r-rank matrix S: k-sparse matrix Side note: Robust PCA “?” • Recovery a low rank matrix L and a sparse matrix S, given M = L + S Robust PCA [Candes et al, 2009] Rank-sparsity incoherence [Chandrasekaran et al, 2011] • We are interested in recovering a low rank matrix L and a sparse matrix S --- not from M --- but from compressive measurements of M Connections to CS and Matrix Completion • If we “remove” L from the optimization, then this reduces to traditional compressive recovery problem • Similarly, if we “remove” S, then this reduces to the Affine rank minimization problem Problem formulation • Key questions – When can we recover L and S ? – Measurement bounds ? – Fast algorithms ? SpaRCS • SpaRCS: Sparse and low Rank recovery from CS – A greedy algorithm – It is an extension of CoSaMP [Tropp and Needell, 2009] and ADMiRA [Lee and Bresler, 2010] SpaRCS • SpaRCS: Sparse and low Rank recovery from CS – A greedy algorithm – It is an extension of CoSAMP [Tropp and Needell, 2009] and ADMiRA [Lee and Bresler, 2010] SpaRCS • SpaRCS: Sparse and low Rank recovery from CS – A greedy algorithm – It is an extension of CoSaMP [Tropp and Needell, 2009] and ADMiRA [Lee and Bresler, 2010] • Claim – If satisfies both RIP and rank-RIP with small constants, – and the low rank matrix is sufficiently dense, and sparse matrix has random support (or bounded col/row degree) – then, SpaRCS converges exponentially to the right answer Phase transitions r=5 r=10 r=15 • p = number of measurements • r = rank, K = sparsity • Matrix of size N x N; N = 512 r=20 r=25 Accuracy Performance Run time CS IT: An alternating projection algorithm that uses soft thresholding at each step CS APG: Variant of APG for RobustPCA problem. Video CS (a) Ground truth (b) Estimated low rank matrix (c) Estimated sparse component Video: 128x128x201 Compression 6.67x SNR = 31.1637 dB Video CS (a) Ground truth (b) Compression 3x Video 64x64x239 Compression 3x SNR = 23.9 dB Hyperspectral recovery results 128x128x128 HS cube Compression 6.67x SNR = 31.1637 dB Accuracy Matrix completion Run time CVX: Interior point solver of convex formulation OptSpace: Non-robust MC solver Open questions • Convergence results for the greedy algorithm • Low rank component is sparse/compressible in a wavelet basis – Is it even possible ? CS-LDS • [S, et al., SIAM J. IS*] • Low rank model – Sparse rows (in a wavelet transformation) • Hyper-spectral data – 2300 Spectral bands – Spatial resolution 128 x 64 – Rank 5 2% Ground Truth 1% M/N = 10% M/N = 2% (rank = 20) M/N = 1% Open questions • Convergence results for the greedy algorithm • Low rank matrix is sparse/compressible in a wavelet basis – Is it even possible ? • Streaming recovery etc… dsp.rice.edu