  In this work, we study the problem of common and unique feature extraction
from noisy data. When we have N observation matrices from N different and
associated sources corrupted by sparse and potentially gross noise, can we
recover the common and unique components from these noisy observations? This is
a challenging task as the number of parameters to estimate is approximately
thrice the number of observations. Despite the difficulty, we propose an
intuitive alternating minimization algorithm called triple component matrix
factorization (TCMF) to recover the three components exactly. TCMF is
distinguished from existing works in literature thanks to two salient features.
First, TCMF is a principled method to separate the three components given noisy
observations provably. Second, the bulk of the computation in TCMF can be
distributed. On the technical side, we formulate the problem as a constrained
nonconvex nonsmooth optimization problem. Despite the intricate nature of the
problem, we provide a Taylor series characterization of its solution by solving
the corresponding Karush-Kuhn-Tucker conditions. Using this characterization,
we can show that the alternating minimization algorithm makes significant
progress at each iteration and converges into the ground truth at a linear
rate. Numerical experiments in video segmentation and anomaly detection
highlight the superior feature extraction abilities of TCMF.
