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You can use PCA to reduce the dimensionality of your descriptors. Note that it won't reduce the number of your descriptors.
For training k-means: you don't need to feed all your descriptors to k-means for BoW tasks. Typical strategies are the use of around 100k descriptors for k-means randomly taken from 1k representative (i.e. from all classes) images, i.e. you need to take only 100 random samples per selected image.
