A classic way to consider a typical text categorization is to
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- Participle, scan all features, create a feature dictionary
- Re-scan all features, use feature dictionaries to map features to feature space numbers to get eigenvectors
- Learning Parameters W
- Storage Learning parameter W, storage feature mapping dictionary
- Predictive truncation Load Learning parameter w, load feature map dictionary
- Scan data, map all features using feature map dictionaries to feature space numbers to get eigenvectors
- Using the Learning parameter w to predict the dot product of the obtained eigenvector
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What do Feature hashing do?
Without the use of the feature dictionary, you do not have to consider the extra space of the storage dictionaries and hash the features directly.
A conflict? It will show that the effect is not very significant!
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With the same memory footprint we can store more weights!
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Feature Hashing Related-1
