Abstract—The recent literature indicates that preserving global pairwise sample similarity is of great importance for feature selection and that many existing selection criteria essentially work in this way. In this paper, we argue that besides global pairwise sample similarity, the local geometric structure of data is also critical and that these two factors play different roles in different learning scenarios. In order to show this, we propose a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection. To demonstrate the generality of our framework, we employ the methods which are well known in the literature to model the local geometric data structure and develop three specific GLSPFS-based feature selection al-gorithms. Also, we develop an efficient optimization algorithm with proved global convergence to solve the resulting feature selection problem. A comprehensive experimental study is then conducted in order to compare our feature selection algorithms with many state-of-the-art ones in supervised, unsupervised and semi-supervised learning scenarios. The result indicates that: (1) Our framework consistently achieves statistically significant improvement in selection performance when compared with currently used algorithms; (2) In supervised and semi-supervised learning scenarios, preserving global pairwise similarity is more important than preserving local geometric data structure; (3) In the unsupervised scenario, preserving local geometric data structure becomes clearly more important; (4) The best feature selection performance is always obtained when the two factors are appropriately integrated. In sum, our work not only validates the advantages of the proposed GLSPFS framework but also gains more insight into the information to be preserved in different feature selection tasks.

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An audio indexing system aims at describing audio content by identifying, labeling or categorizing different acoustic events. Since the resulting audio classification and indexing is meant for direct human consumption, it is highly desirable that it produces perceptually relevant results. This can be obtained by integrating specific knowledge of the human auditory system in the design process to various extent. In this paper, we highlight some of the important concepts used in audio classification and indexing that are perceptually motivated or that exploit some principles of perception. In particular, we discuss several different strategies to integrate human perception including 1) the use of generic audition models, 2) the use of perceptually-relevant features for the analysis stage that are perceptually justified either as a component of a hearing model or as being correlated with a perceptual dimension of sound similarity, and 3) the involvement of the user in the audio indexing or classification task. In the paper, we also illustrate some of the recent trends in semantic audio retrieval that approximate higher level perceptual processing and cognitive aspects of human audio recognition capabilities including affect-based audio retrieval.