How to use classification after PCA(dimensionality reduction)
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Kong on 25 Mar 2020
Commented: Kong on 17 Apr 2020
I want to classify videos.
The original data : 25290 x 25
After applying dimensionality reduction like PCA, I got this data.
The reduced data : 2 x 25
Is it resonable to use these reduced data for classification?
I got low accuracy of classification. Could you give some ideas to solve it?
Shishir Singhal on 9 Apr 2020
Don't use PCA for dimentionality reduction or feature selection in case of videos or images. Instead use some feature extraction techniques in images like HOG features, sift features, optical flow(for videos), etc. Since the datapoints are very less(assuming 25 datapoints), you could use some data augementation techniques for videos.
For example, crop some random sequence from the video for temporal augmentation then use Image augmentation for each frame.
For Image augmentation, you can refer to documentation here: https://www.mathworks.com/help/deeplearning/ref/imagedataaugmenter.html
Hope it helps !!! :)
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