89,247 research outputs found
CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size
Let
where is a matrix, consisting of independent and
identically distributed (i.i.d.) real random variables with mean zero
and variance one. When , under fourth moment conditions a central
limit theorem (CLT) for linear spectral statistics (LSS) of
defined by the eigenvalues is established. We also explore its applications in
testing whether a population covariance matrix is an identity matrix.Comment: Published at http://dx.doi.org/10.3150/14-BEJ599 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Learning from Ontology Streams with Semantic Concept Drift
Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing
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