Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction
This work addresses overfitting and interpretability issues in audio and visual feature extraction, but it is incremental as it builds on existing orthonormalized partial least-squares methods.
The authors tackled the problem of high-dimensional nonnegative signal analysis by proposing a supervised method for designing filter banks that ensures nonnegativity and interpretability, achieving competitive performance in texture and music genre classification compared to state-of-the-art methods.
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.