  In this work, a novel rank-revealing matrix decomposition algorithm termed
Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant
aided by the power method technique is proposed. CoR-UTV computes an
approximation to a low-rank input matrix by making use of random sampling
schemes. Given a large and dense matrix of size $m\times n$ with numerical rank
$k$, where $k \ll \text{min} \{m,n\}$, CoR-UTV requires a few passes over the
data, and runs in $O(mnk)$ floating-point operations. Furthermore, CoR-UTV can
exploit modern computational platforms and can be optimized for maximum
efficiency. CoR-UTV is also applied for solving robust principal component
analysis problems. Simulations show that CoR-UTV outperform existing
approaches.
