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L1 regularisation is the usual way (see e.g. https://en.wikipedia.org/wiki/Lasso_(statistics) https://en.wikipedia.org/wiki/Lasso_(statistics))
by z2210558 4y ago
L1 regularisation is the usual way (see e.g. https://en.wikipedia.org/wiki/Lasso_(statistics) https://en.wikipedia.org/wiki/Lasso_(statistics))
- ylks 4y agoFeature selection ought to be model-specific. Because a feature wasn't selected by Lasso (in a linear model) does not mean it cannot be useful in a non-linear model.
- microtonal 4y agoThere are also some iterative methods like grafting (using feature gradients): https://www.jmlr.org/papers/volume3/perkins03a/perkins03a.pdf https://www.jmlr.org/papers/volume3/perkins03a/perkins03a.pd... and gain-based selection (using the improvement of the objective), see the appendix of: https://aclanthology.org/J96-1002.pdf https://aclanthology.org/J96-1002.pdf We used grafting for parser feature selection, for which it worked quite well: https://danieldk.eu/Research/Publications/ucnlg2011.pdf https://danieldk.eu/Research/Publications/ucnlg2011.pdf
- clove 4y agoPsychologists use factor analysis.