Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparsity-based Cholesky Factorization and its Application to
  Hyperspectral Anomaly Detection   Estimating large covariance matrices has been a longstanding important
problem in many applications and has attracted increased attention over several
decades. This paper deals with two methods based on pre-existing works to
impose sparsity on the covariance matrix via its unit lower triangular matrix
(aka Cholesky factor) $\mathbf{T}$. The first method serves to estimate the
entries of $\mathbf{T}$ using the Ordinary Least Squares (OLS), then imposes
sparsity by exploiting some generalized thresholding techniques such as Soft
and Smoothly Clipped Absolute Deviation (SCAD). The second method directly
estimates a sparse version of $\mathbf{T}$ by penalizing the negative normal
log-likelihood with $L_1$ and SCAD penalty functions. The resulting covariance
estimators are always guaranteed to be positive definite. Some Monte-Carlo
simulations as well as experimental data demonstrate the effectiveness of our
estimators for hyperspectral anomaly detection using the Kelly anomaly
detector.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: cs
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org