2,381 research outputs found
Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach
This paper investigates the fundamental coupling between loads and locational
marginal prices (LMPs) in security-constrained economic dispatch (SCED).
Theoretical analysis based on multi-parametric programming theory points out
the unique one-to-one mapping between load and LMP vectors. Such one-to-one
mapping is depicted by the concept of system pattern region (SPR) and
identifying SPRs is the key to understanding the LMP-load coupling. Built upon
the characteristics of SPRs, the SPR identification problem is modeled as a
classification problem from a market participant's viewpoint, and a Support
Vector Machine based data-driven approach is proposed. It is shown that even
without the knowledge of system topology and parameters, the SPRs can be
estimated by learning from historical load and price data. Visualization and
illustration of the proposed data-driven approach are performed on a 3-bus
system as well as the IEEE 118-bus system
Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data
The recent development of more sophisticated spectroscopic methods allows acquisition of high dimensional datasets from which valuable information may be extracted using multivariate statistical analyses, such as dimensionality reduction and automatic classification (supervised and unsupervised). In this work, a supervised classification through a partial least squares discriminant analysis (PLS-DA) is performed on the hy- perspectral data. The obtained results are compared with those obtained by the most commonly used classification approaches
Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data
The recent development of more sophisticated spectroscopic methods allows
acqui- sition of high dimensional datasets from which valuable information may
be extracted using multivariate statistical analyses, such as dimensionality
reduction and automatic classification (supervised and unsupervised). In this
work, a supervised classification through a partial least squares discriminant
analysis (PLS-DA) is performed on the hy- perspectral data. The obtained
results are compared with those obtained by the most commonly used
classification approaches
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Support vector machines (SVMs) are an important tool in modern data analysis.
Traditionally, support vector machines have been fitted via quadratic
programming, either using purpose-built or off-the-shelf algorithms. We present
an alternative approach to SVM fitting via the majorization--minimization (MM)
paradigm. Algorithms that are derived via MM algorithm constructions can be
shown to monotonically decrease their objectives at each iteration, as well as
be globally convergent to stationary points. We demonstrate the construction of
iteratively-reweighted least-squares (IRLS) algorithms, via the MM paradigm,
for SVM risk minimization problems involving the hinge, least-square,
squared-hinge, and logistic losses, and 1-norm, 2-norm, and elastic net
penalizations. Successful implementations of our algorithms are presented via
some numerical examples
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