Local Reconstruction Error Alignment: A Fast Unsupervised Feature Selection Algorithm for Radar Target Clustering

Jianqiao WANG  Yuehua LI  Jianfei CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D    No.2    pp.357-360
Publication Date: 2014/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.357
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
feature selection,  local reconstruction,  manifold,  alignment,  

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Observed samples in wideband radar are always represented as nonlinear points in high dimensional space. In this paper, we consider the feature selection problem in the scenario of wideband radar target clustering. Inspired by manifold learning, we propose a novel feature selection algorithm, called Local Reconstruction Error Alignment (LREA), to select the features that can best preserve the underlying manifold structure. We first select the features that minimize the reconstruction error in every neighborhood. Then, we apply the alignment technique to extend the local optimal feature sequence to a global unique feature sequence. Experiments demonstrate the effectiveness of our proposed method.