Face recognition using semi-supervised spectral feature selection

Zhihong Zhang, Edwin R. Hancock

Abstract: Semi-supervised learning is important when labeled data are scarce. In this paper, we develop a novel semi-supervised spectral feature selection technique using label regression and by using l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for semi-supervised feature selection. In the first step, we use label propagation and label regression to transform the data into a lower-dimensional space so as to improve class separation. Second, we use l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm regularization to select the features that best align with the lower-dimensional data. Using l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm regularization, we cast feature discriminant analysis into a regression framework which accommodates the correlations among features. As a result, we can evaluate joint feature combinations, rather than being confined to consider them individually. Experimental results demonstrate the effectiveness of our feature selection method on standard face data-sets.