A discriminant kernel entropy-based framework for feature representation learning

Abstract The intelligent multimedia processing community has developed increasing interest in kernel entropy component analysis (KECA) due to its abilities in effective data transformation and dimensionality reduction. However, since only the unsupervised structural information of Renyi entropy from the given data set is utilized, KECA alone is incapable of generating high quality discriminant features. Aiming to develop a new and generic approach for feature representation learning, this paper proposes a discriminant kernel entropy-based framework, which integrates KECA and a complete discriminant strategy (consisting of regular and irregular discriminant information), to explore discriminant feature representations from the given data set. The framework is realized and further optimized to generate a more powerful discriminant descriptor for feature representation learning, leading to improved performance. Since the joint utilization of kernel entropy estimation and the complete discriminant strategy is able to reveal the distribution and semantic information of the given data, the proposed framework opens up a new front for discriminant feature representation learning via information theoretic learning (ITL). To demonstrate the generic nature and effectiveness of the proposed framework, experiments are conducted on two different data sources; the visual data source (e.g., University of California Irvine (UCI) database, Olivetti Research Lab (ORL) database, Caltech 256 database) and the audio data source (Ryerson Multimedia Lab (RML) audio emotion database). The results show this framework yields superior performance over other methods on the data sets evaluated for feature representation learning.