Feature Vector Hashing Task
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A Feature Vector Hashing Task is a featurization task that can produce hashed feature (attributes are mapped via a hash).
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- Counter-Example(s):
- See: Hashing, Predictor Feature, Dimensionality Reduction, Bloom Filter, Learning with Counts, Count-Min Sketches.
References
2013
- http://en.wikipedia.org/wiki/Feature_hashing
- In machine learning, feature hashing, also known as the hashing trick, is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values as indices directly, rather than looking the indices up in an associative array.
2009
- (Weinberger et al., 2009) ⇒ Kilian Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, and Josh Attenberg. (2009). “Feature Hashing for Large Scale Multitask Learning.” In: Proceedings of the 26th Annual International Conference on Machine Learning. doi:10.1145/1553374.1553516