  Audio or visual data analysis tasks usually have to deal with
high-dimensional and nonnegative signals. However, most data analysis methods
suffer from overfitting and numerical problems when data have more than a few
dimensions needing a dimensionality reduction preprocessing. Moreover,
interpretability about how and why filters work for audio or visual
applications is a desired property, especially when energy or spectral signals
are involved. In these cases, due to the nature of these signals, the
nonnegativity of the filter weights is a desired property to better understand
its working. Because of these two necessities, we propose different methods to
reduce the dimensionality of data while the nonnegativity and interpretability
of the solution are assured. In particular, we propose a generalized
methodology to design filter banks in a supervised way for applications dealing
with nonnegative data, and we explore different ways of solving the proposed
objective function consisting of a nonnegative version of the orthonormalized
partial least-squares method. We analyze the discriminative power of the
features obtained with the proposed methods for two different and widely
studied applications: texture and music genre classification. Furthermore, we
compare the filter banks achieved by our methods with other state-of-the-art
methods specifically designed for feature extraction.
