3 research outputs found
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis
Subspace recovery from corrupted and missing data is crucial for various
applications in signal processing and information theory. To complete missing
values and detect column corruptions, existing robust Matrix Completion (MC)
methods mostly concentrate on recovering a low-rank matrix from few corrupted
coefficients w.r.t. standard basis, which, however, does not apply to more
general basis, e.g., Fourier basis. In this paper, we prove that the range
space of an matrix with rank can be exactly recovered from few
coefficients w.r.t. general basis, though and the number of corrupted
samples are both as high as . Our model covers
previous ones as special cases, and robust MC can recover the intrinsic matrix
with a higher rank. Moreover, we suggest a universal choice of the
regularization parameter, which is . By our
filtering algorithm, which has theoretical guarantees, we can
further reduce the computational cost of our model. As an application, we also
find that the solutions to extended robust Low-Rank Representation and to our
extended robust MC are mutually expressible, so both our theory and algorithm
can be applied to the subspace clustering problem with missing values under
certain conditions. Experiments verify our theories.Comment: To appear in IEEE Transactions on Information Theor
Noise-Stable Rigid Graphs for Euclidean Embedding
We proposed a new criterion \textit{noise-stability}, which revised the
classical rigidity theory, for evaluation of MDS algorithms which can
truthfully represent the fidelity of global structure reconstruction; then we
proved the noise-stability of the cMDS algorithm in generic conditions, which
provides a rigorous theoretical guarantee for the precision and theoretical
bounds for Euclidean embedding and its application in fields including wireless
sensor network localization and satellite positioning.
Furthermore, we looked into previous work about minimum-cost globally rigid
spanning subgraph, and proposed an algorithm to construct a minimum-cost
noise-stable spanning graph in the Euclidean space, which enabled reliable
localization on sparse graphs of noisy distance constraints with linear numbers
of edges and sublinear costs in total edge lengths. Additionally, this
algorithm also suggests a scheme to reconstruct point clouds from pairwise
distances at a minimum of time complexity, down from for cMDS