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You should normalize every column individually. It will work just fine. Sum up the column and divide every element of that column by sum of that column. But as your feature 2,3,4 are of very small scale you should consider some transformation like log transformation as you might encounter numerical underflow.


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You should check the distribution of each feature and scale them accordingly, but in any case you should aim to roughly the same interval of values for every feature. For example, if f1 has the standard distribution and f2 is close to the uniform one, then you can scale f1 to N(0,1) and f2 to U(-1,1). In other words, try to have maximum, minimum and mean ...


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Introduction Bag-of-features (BoF) (also known as bag-of-visual-words) is a method to represent the features of images (i.e. a feature extraction/generation/representation algorithm). BoF is inspired by the bag-of-words model often used in the context of NLP, hence the name. In the context of computer vision, BoF can be used for different purposes, such as ...


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