Mar 28, 2014 · Title:Learning detectors quickly using structured covariance matrices. Authors:Jack Valmadre, Sridha Sridharan, Simon Lucey. View a PDF of the ...
This paper elucidate an important connection to the correlation filter literature, demonstrating that these can be trained without ever revisiting the ...
Detectors. Article. Learning detectors quickly using structured covariance matrices. March 2014. Source; arXiv. Authors: Jack Valmadre at University of Adelaide.
Dec 14, 2020 · The novel approach increases the classification performance by up to four percentage points for small training datasets.
We present an expression for the covariance matrix of the set of state vectors describing a track fitted with a Kalman filter.
Jun 4, 2021 · They absolutely can. The main reason behind learning a diagonal covariance instead of a full covariance matrix is that you want to enforce basic properties for ...
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In this paper, we aim to study multiple structural breaks in the covariance structure of Xt generated by the approximate factor model, estimating the break ...
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In the realm of high-dimensional data analysis, understanding the co- variance structure is fundamental. Covariance matrices underpin numerous statistical meth-.
Abstract. Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using.
The MAP and ML solutions with the non-convex rank constraint are shown to be a simple scalar thresholding of eigenvalues of a suitably translated and projected.