21 research outputs found
Provable Dynamic Robust PCA or Robust Subspace Tracking
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust
PCA (RPCA). It assumes that the true (uncorrupted) data lies in a
low-dimensional subspace that can change with time, albeit slowly. The goal is
to track this changing subspace over time in the presence of sparse outliers.
We develop and study a novel algorithm, that we call simple-ReProCS, based on
the recently introduced Recursive Projected Compressive Sensing (ReProCS)
framework. Our work provides the first guarantee for dynamic RPCA that holds
under weakened versions of standard RPCA assumptions, slow subspace change and
a lower bound assumption on most outlier magnitudes. Our result is significant
because (i) it removes the strong assumptions needed by the two previous
complete guarantees for ReProCS-based algorithms; (ii) it shows that it is
possible to achieve significantly improved outlier tolerance, compared with all
existing RPCA or dynamic RPCA solutions by exploiting the above two simple
extra assumptions; and (iii) it proves that simple-ReProCS is online (after
initialization), fast, and, has near-optimal memory complexity.Comment: Minor writing edits. The paper has been accepted to IEEE Transactions
on Information Theor
Fast Robust Subspace Tracking via PCA in Sparse Data-Dependent Noise
This work studies the robust subspace tracking (ST) problem. Robust ST can be
simply understood as a (slow) time-varying subspace extension of robust PCA. It
assumes that the true data lies in a low-dimensional subspace that is either
fixed or changes slowly with time. The goal is to track the changing subspaces
over time in the presence of additive sparse outliers and to do this quickly
(with a short delay). We introduce a "fast" mini-batch robust ST solution that
is provably correct under mild assumptions. Here "fast" means two things: (i)
the subspace changes can be detected and the subspaces can be tracked with
near-optimal delay, and (ii) the time complexity of doing this is the same as
that of simple (non-robust) PCA. Our main result assumes piecewise constant
subspaces (needed for identifiability), but we also provide a corollary for the
case when there is a little change at each time.
A second contribution is a novel non-asymptotic guarantee for PCA in linearly
data-dependent noise. An important setting where this is useful is for linearly
data dependent noise that is sparse with support that changes enough over time.
The analysis of the subspace update step of our proposed robust ST solution
uses this result.Comment: To appear in IEEE Journal of Special Areas in Information Theor
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
PCA is one of the most widely used dimension reduction techniques. A related
easier problem is "subspace learning" or "subspace estimation". Given
relatively clean data, both are easily solved via singular value decomposition
(SVD). The problem of subspace learning or PCA in the presence of outliers is
called robust subspace learning or robust PCA (RPCA). For long data sequences,
if one tries to use a single lower dimensional subspace to represent the data,
the required subspace dimension may end up being quite large. For such data, a
better model is to assume that it lies in a low-dimensional subspace that can
change over time, albeit gradually. The problem of tracking such data (and the
subspaces) while being robust to outliers is called robust subspace tracking
(RST). This article provides a magazine-style overview of the entire field of
robust subspace learning and tracking. In particular solutions for three
problems are discussed in detail: RPCA via sparse+low-rank matrix decomposition
(S+LR), RST via S+LR, and "robust subspace recovery (RSR)". RSR assumes that an
entire data vector is either an outlier or an inlier. The S+LR formulation
instead assumes that outliers occur on only a few data vector indices and hence
are well modeled as sparse corruptions.Comment: To appear, IEEE Signal Processing Magazine, July 201
Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data
Subspace tracking (ST) with missing data (ST-miss) or outliers (Robust ST) or
both (Robust ST-miss) has been extensively studied in the last many years. This
work provides a new simple algorithm and guarantee for both ST with missing
data (ST-miss) and RST-miss. Unlike past work on this topic, the algorithm is
much simpler (uses fewer parameters) and the guarantee does not make the
artificial assumption of piecewise constant subspace change, although it still
handles that setting. Secondly, we extend our approach and its analysis to
provably solving these problems when the raw data is federated and when the
over-air data communication modality is used for information exchange between
the peer nodes and the center.Comment: New model, algorithm for centralized case; added algorithms to deal
with sparse outliers; modified organization significantl
Matrix Approximation with Side Information: When Column Sampling is Enough
A novel matrix approximation problem is considered herein: observations based
on a few fully sampled columns and quasi-polynomial structural side information
are exploited. The framework is motivated by quantum chemistry problems wherein
full matrix computation is expensive, and partial computations only lead to
column information. The proposed algorithm successfully estimates the column
and row-space of a true matrix given a priori structural knowledge of the true
matrix. A theoretical spectral error bound is provided, which captures the
possible inaccuracies of the side information. The error bound proves it scales
in its signal-to-noise (SNR) ratio. The proposed algorithm is validated via
simulations which enable the characterization of the amount of information
provided by the quasi-polynomial side information