1,009 research outputs found
Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation
Gini distance correlation (GDC) was recently proposed to measure the
dependence between a categorical variable, Y, and a numerical random vector, X.
It mutually characterizes independence between X and Y. In this article, we
utilize the GDC to establish a feature screening for ultrahigh-dimensional
discriminant analysis where the response variable is categorical. It can be
used for screening individual features as well as grouped features. The
proposed procedure possesses several appealing properties. It is model-free. No
model specification is needed. It holds the sure independence screening
property and the ranking consistency property. The proposed screening method
can also deal with the case that the response has divergent number of
categories. We conduct several Monte Carlo simulation studies to examine the
finite sample performance of the proposed screening procedure. Real data
analysis for two real life datasets are illustrated.Comment: 25 pages, 1 figur
A note on eigenvalues of random block Toeplitz matrices with slowly growing bandwidth
This paper can be thought of as a remark of \cite{llw}, where the authors
studied the eigenvalue distribution of random block Toeplitz band
matrices with given block order . In this note we will give explicit density
functions of when the bandwidth grows
slowly. In fact, these densities are exactly the normalized one-point
correlation functions of Gaussian unitary ensemble (GUE for short).
The series can be seen
as a transition from the standard normal distribution to semicircle
distribution. We also show a similar relationship between GOE and block
Toeplitz band matrices with symmetric blocks.Comment: 6 page
Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power
Recently, with the rapid development of autonomous vehicles and connected
vehicles, the demands of vehicular computing keep continuously growing. We
notice a constant and limited onboard computational ability can hardly keep up
with the rising requirements of the vehicular system and software application
during their long-term lifetime, and also at the same time, the vehicles
onboard computation causes an increasingly higher vehicular energy consumption.
Therefore, we suppose to build a vehicular edge cloud computing (VECC)
framework to resolve such a vehicular computing dilemma. In this framework,
potential vehicular computing tasks can be executed remotely in an edge cloud
within their time latency constraints. Simultaneously, an effective wireless
network resources allocation scheme is one of the essential and fundamental
factors for the QoS (quality of Service) on the VECC. In this paper, we adopted
a stochastic fair allocation (SFA) algorithm to randomly allocate minimum
required resource blocks to admitted vehicular users. The numerical results
show great effectiveness of energy efficiency in VECC.Comment: 2018 IEEE 21st International Conference on Intelligent Transportation
Systems (ITSC
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