Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MCA Learning Algorithm for Incident Signals Estimation: A Review   Recently there has been many works on adaptive subspace filtering in the
signal processing literature. Most of them are concerned with tracking the
signal subspace spanned by the eigenvectors corresponding to the eigenvalues of
the covariance matrix of the signal plus noise data. Minor Component Analysis
(MCA) is important tool and has a wide application in telecommunications,
antenna array processing, statistical parametric estimation, etc. As an
important feature extraction technique, MCA is a statistical method of
extracting the eigenvector associated with the smallest eigenvalue of the
covariance matrix. In this paper, we will present a MCA learning algorithm to
extract minor component from input signals, and the learning rate parameter is
also presented, which ensures fast convergence of the algorithm, because it has
direct effect on the convergence of the weight vector and the error level is
affected by this value. MCA is performed to determine the estimated DOA.
Simulation results will be furnished to illustrate the theoretical results
achieved.

29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics