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• W2514862016 abstract "Low-level feature encoding combined with Spatial Pyramid Matching (SPM) is widely adopted in the image classification system nowadays to extract features, which are usually high-dimensional. This not only makes the classification problem computationally prohibitive, but also raises other issues, such as the “curse of dimensionality”. In this paper we present supervised dimensionality reduction (DR) approaches that try to maximize the mutual information (MI) between the class labels and the low-dimensional features through an orthonormal linear projection. In addition to reduced computational load, using the extracted low-dimensional features during classification also leads to higher accuracies, as evident from a series of image classification experiments in this paper." @default.
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• W2514862016 date "2016-09-01" @default.
• W2514862016 modified "2023-09-23" @default.
• W2514862016 title "Dimensionality reduction for image classification via mutual information maximization" @default.
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• W2514862016 doi "https://doi.org/10.1109/icip.2016.7532409" @default.
• W2514862016 hasPublicationYear "2016" @default.
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