- Read more about Estimating Structural Missing Values via Low-tubal-rank Tensor Completion
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The recently proposed Tensor Nuclear Norm (TNN) minimization has been widely used for tensor completion. However, previous works didn’t consider the structural difference between the observed data and missing data, which widely exists in many applications. In this paper, we propose to incorporate a constraint item on the missing values into low-tubal-rank tensor completion to promote the structural hypothesis

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- Read more about Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking
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In this paper, we extend previous particle filtering methods whose states were constrained to the (real) Stiefel manifold to the complex case. The method is then applied to a Bayesian formulation of the subspace tracking problem. To implement the proposed particle filter, we modify a previous MCMC algorithm so as to simulate from densities defined on the complex manifold. Also, to compute subspace estimates from particle approximations, we extend existing averaging methods to complex Grassmannians.

## slides.pdf

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- Read more about On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data
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- Read more about A whiteness test based on the spectral measure of large non-Hermitian random matrices
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In the context of multivariate time series, a whiteness test against an MA(1)

correlation model is proposed. This test is built on the eigenvalue

distribution (spectral measure) of the non-Hermitian one-lag sample

autocovariance matrix, instead of its singular value distribution. The large

dimensional limit spectral measure of this matrix is derived. To obtain this

result, a control over the smallest singular value of a related random matrix

is provided. Numerical simulations show the excellent performance of this

test.

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- Read more about ROBUST M-ESTIMATION BASED MATRIX COMPLETION
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Conventional approaches to matrix completion are sensitive to outliers and impulsive noise. This paper develops robust and computationally efficient M-estimation based matrix completion algorithms. By appropriately arranging the observed entries, and then applying alternating minimization, the robust matrix completion problem is converted into a set of regression M-estimation problems. Making use of differ- entiable loss functions, the proposed algorithm overcomes a weakness of the lp-loss (p ≤ 1), which easily gets stuck in an inferior point.

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In this paper, we study the denoising of piecewise smooth graph sig-nals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of non-convex regularizers that exhibit superior recovery performance overexisting convex ones. We present theoretical results in the form ofasymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposedoptimization problem and analyze its convergence.

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- Read more about PERFORMANCE BOUND FOR BLIND EXTRACTION OF NON-GAUSSIAN COMPLEX-VALUED VECTOR COMPONENT FROM GAUSSIAN BACKGROUND
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Independent Vector Extraction aims at the joint blind source extraction of $K$ dependent signals of interest (SOI) from $K$ mixtures (one signal from one mixture). Similarly to Independent Component/Vector Analysis (ICA/IVA), the SOIs are assumed to be independent of the other signals in the mixture. Compared to IVA, the (de-)mixing IVE model is reduced in the number of parameters for the extraction problem. The SOIs are assumed to be non-Gaussian or noncircular Gaussian, while the other signals are modeled as circular Gaussian.

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- Read more about Updates In Bayesian Filtering By Continuous Projections On A Manifold Of Densities
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In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The projection filtering framework is exploited to develop accurate approximations of posterior distributions within parametric classes of probability distributions. This is done by formulating an ordinary differential equation for the posterior distribution that has the prior as initial value and hits the exact posterior after a unit of

## poster.pdf

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