Heteroscedastic linear feature extraction based on sufficiency conditions

Classification of high-dimensional data typically requires extraction of discriminant features. This paper proposes a linear feature extractor, called whitened linear sufficient statistic (WLSS), which is based on the sufficiency conditions for heteroscedastic Gaussian distributions. WLSS approximates, in the least squares sense, an operator providing a sufficient statistic. The proposed method retains covariance discriminance in heteroscedastic data, while it reduces to the commonly used linear discriminant analysis (LDA) in the homoscedastic case. Compared to similar heteroscedastic methods, WLSS imposes a low computational complexity, and is highly generalizable as confirmed by its consistent competence over various data sets.

► WLSS is a non-iterative heteroscedastic linear feature extraction method.
► It approximates, in least squares, a linear operator giving a sufficient statistic.
► Its consistent competence over various data sets shows its high generalizability.
► It imposes lowest computational complexity among similar heteroscedastic methods.