Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features

22 Jun 2020Francisco J. H. HerasGonzalo G. de Polavieja

We explore the application of linear discriminant analysis (LDA) to the features obtained in different layers of pretrained deep convolutional neural networks (CNNs). The advantage of LDA compared to other techniques in dimensionality reduction is that it reduces dimensions while preserving the global structure of data, so distances in the low-dimensional structure found are meaningful... (read more)

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