6,373 research outputs found
DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets
Principal component analysis (PCA) has well-documented merits for data
extraction and dimensionality reduction. PCA deals with a single dataset at a
time, and it is challenged when it comes to analyzing multiple datasets. Yet in
certain setups, one wishes to extract the most significant information of one
dataset relative to other datasets. Specifically, the interest may be on
identifying, namely extracting features that are specific to a single target
dataset but not the others. This paper develops a novel approach for such
so-termed discriminative data analysis, and establishes its optimality in the
least-squares (LS) sense under suitable data modeling assumptions. The
criterion reveals linear combinations of variables by maximizing the ratio of
the variance of the target data to that of the remainders. The novel approach
solves a generalized eigenvalue problem by performing SVD just once. Numerical
tests using synthetic and real datasets showcase the merits of the proposed
approach relative to its competing alternatives.Comment: 5 pages, 2 figure
Moving-Horizon Dynamic Power System State Estimation Using Semidefinite Relaxation
Accurate power system state estimation (PSSE) is an essential prerequisite
for reliable operation of power systems. Different from static PSSE, dynamic
PSSE can exploit past measurements based on a dynamical state evolution model,
offering improved accuracy and state predictability. A key challenge is the
nonlinear measurement model, which is often tackled using linearization,
despite divergence and local optimality issues. In this work, a moving-horizon
estimation (MHE) strategy is advocated, where model nonlinearity can be
accurately captured with strong performance guarantees. To mitigate local
optimality, a semidefinite relaxation approach is adopted, which often provides
solutions close to the global optimum. Numerical tests show that the proposed
method can markedly improve upon an extended Kalman filter (EKF)-based
alternative.Comment: Proc. of IEEE PES General Mtg., Washnigton, DC, July 27-31, 2014.
(Submitted
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