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Given a pre-trained CNN model, I extract feature vector of images in reference and query dataset with several thousands of elements.

I would like to apply some augmentation techniques to reduce the feature vector dimension to speed up cosine similarity/euclidean distance matrix calculation.

I have already come up with the following two methods in my literature review:

- Principal Component Analysis (PCA) + Whitening
- Locality Search Hashing (LSH)

Are there more approaches to perform dimensionality reduction of feature vectors? If so, what are the pros/cons of each perhaps?

1Maybe this answer could be further improved if you link to a paper or implementation that shows the application of AE to reduce the dimensionality of feature vectors. If you consider images feature vectors, then, in a way, AE are commonly applied to reduce the dimensionality of images (or feature vectors), but what if the inputs are not images? – nbro – 2020-01-26T23:28:10.927

Is there any python library perform autoencoder efficiently? – Färid Alijani – 2020-02-03T06:44:42.500

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@FäridAlijani Not that I know of. However, designing one in Keras wouldn't be much of a task. The following Keras blog might help : https://blog.keras.io/building-autoencoders-in-keras.html

– SoumyadeepB – 2020-02-03T21:18:47.780