In this paper related to factorization machine, the author compares factorization machine (FM) with SVM. As FM performs better than SVM, it's considered state of the art for sparse data. Why SVM is considered so important algorithm for sparse dataset? What features of SVM makes it superior for sparse data?
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$\begingroup$ I think this could be a good question if you added some more detail and if you could narrow down your question :) $\endgroup$– oW_ ♦Aug 10, 2017 at 22:12
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1 Answer
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Support Vector Machines (SVM) represent data examples as points in space and tries to create a mapping with a wide as possible gap between the separate categories. The data examples closest to the gap are called "support vectors". Those support vectors define the SVM classifier.
If you are able to identify the support vectors, you can ignore all other data points and dimensions. Thus SVM can efficiently handle sparse data.
answered Aug 10, 2017 at 16:24
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$\begingroup$ The answer makes no sense. How can you ignore dimensions when identifying a point in space? $\endgroup$– sandypAug 2, 2018 at 16:03
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1$\begingroup$ If the matrix is not full rank, dimensions are a linear combination of other dimensions. Those dimensions can be ignored because the information of those features are modeled by the other dimensions. $\endgroup$ Aug 2, 2018 at 16:16