18,723 research outputs found
Generalized Weiszfeld algorithms for Lq optimization
In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L₂ cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q <; 2, while maintaining simplicity and provable convergence. We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L₂ optimization.This research has been funded by National ICT Australia
Load curve data cleansing and imputation via sparsity and low rank
The smart grid vision is to build an intelligent power network with an
unprecedented level of situational awareness and controllability over its
services and infrastructure. This paper advocates statistical inference methods
to robustify power monitoring tasks against the outlier effects owing to faulty
readings and malicious attacks, as well as against missing data due to privacy
concerns and communication errors. In this context, a novel load cleansing and
imputation scheme is developed leveraging the low intrinsic-dimensionality of
spatiotemporal load profiles and the sparse nature of "bad data.'' A robust
estimator based on principal components pursuit (PCP) is adopted, which effects
a twofold sparsity-promoting regularization through an -norm of the
outliers, and the nuclear norm of the nominal load profiles. Upon recasting the
non-separable nuclear norm into a form amenable to decentralized optimization,
a distributed (D-) PCP algorithm is developed to carry out the imputation and
cleansing tasks using networked devices comprising the so-termed advanced
metering infrastructure. If D-PCP converges and a qualification inequality is
satisfied, the novel distributed estimator provably attains the performance of
its centralized PCP counterpart, which has access to all networkwide data.
Computer simulations and tests with real load curve data corroborate the
convergence and effectiveness of the novel D-PCP algorithm.Comment: 8 figures, submitted to IEEE Transactions on Smart Grid - Special
issue on "Optimization methods and algorithms applied to smart grid
A numerically efficient implementation of the expectation maximization algorithm for state space models
Peer reviewedPostprin
Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization
Generalized Linear Models (GLMs), where a random vector is
observed through a noisy, possibly nonlinear, function of a linear transform
arise in a range of applications in nonlinear
filtering and regression. Approximate Message Passing (AMP) methods, based on
loopy belief propagation, are a promising class of approaches for approximate
inference in these models. AMP methods are computationally simple, general, and
admit precise analyses with testable conditions for optimality for large i.i.d.
transforms . However, the algorithms can easily diverge for general
. This paper presents a convergent approach to the generalized AMP
(GAMP) algorithm based on direct minimization of a large-system limit
approximation of the Bethe Free Energy (LSL-BFE). The proposed method uses a
double-loop procedure, where the outer loop successively linearizes the LSL-BFE
and the inner loop minimizes the linearized LSL-BFE using the Alternating
Direction Method of Multipliers (ADMM). The proposed method, called ADMM-GAMP,
is similar in structure to the original GAMP method, but with an additional
least-squares minimization. It is shown that for strictly convex, smooth
penalties, ADMM-GAMP is guaranteed to converge to a local minima of the
LSL-BFE, thus providing a convergent alternative to GAMP that is stable under
arbitrary transforms. Simulations are also presented that demonstrate the
robustness of the method for non-convex penalties as well
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