1,616 research outputs found
Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation
problems based on partial observations. Existing matrix factorization is based
on least squares and aims to yield a low-rank matrix to interpret the
conditional sample means given the observations. However, in many real
applications with skewed and extreme data, least squares cannot explain their
central tendency or tail distributions, yielding undesired estimates. In this
paper, we propose \emph{expectile matrix factorization} by introducing
asymmetric least squares, a key concept in expectile regression analysis, into
the matrix factorization framework. We propose an efficient algorithm to solve
the new problem based on alternating minimization and quadratic programming. We
prove that our algorithm converges to a global optimum and exactly recovers the
true underlying low-rank matrices when noise is zero. For synthetic data with
skewed noise and a real-world dataset containing web service response times,
the proposed scheme achieves lower recovery errors than the existing matrix
factorization method based on least squares in a wide range of settings.Comment: 8 page main text with 5 page supplementary documents, published in
AAAI 201
Growth of Large Domain Epitaxial Graphene on the C-Face of SiC
Growth of epitaxial graphene on the C-face of SiC has been investigated.
Using a confinement controlled sublimation (CCS) method, we have achieved well
controlled growth and been able to observe propagation of uniform monolayer
graphene. Surface patterns uncover two important aspects of the growth, i.e.
carbon diffusion and stoichiometric requirement. Moreover, a new "stepdown"
growth mode has been discovered. Via this mode, monolayer graphene domains can
have an area of hundreds of square micrometers, while, most importantly, step
bunching is avoided and the initial uniformly stepped SiC surface is preserved.
The stepdown growth provides a possible route towards uniform epitaxial
graphene in wafer size without compromising the initial flat surface morphology
of SiC.Comment: 18 pages, 8 figure
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