Timeline for Clustering with Only Categorical Features
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 15, 2020 at 21:53 | comment | added | Corey Levinson | For autoencoding, the idea is to have 3 layers: the input layer consisting of all of your features, then a layer with smaller number of units, and then try to recreate the input layer again in the 3rd layer. After training this model, you take the representation of your data in the 2nd layer and it is a "compressed" form. Honestly that is a really overkill in my opinion, why can't you just do PCA or SVD if you are concerned with sparsity | |
| Mar 18, 2020 at 18:08 | comment | added | OmG |
@formicaman oops! You are in the wrong way! scipy.sparse.issparse is for the type of the variable, not about the concept of matrix sparsity!
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| Mar 18, 2020 at 17:47 | comment | added | formicaman | I used 'scipy.sparse.issparse(X_encoded)' and it returned 'False'. But I am looking at blog.keras.io/building-autoencoders-in-keras.html for auto-encoding. | |
| Mar 18, 2020 at 17:22 | comment | added | OmG | @formicaman as you described the data, after the encoding, it will be sparse. For the auto encoding, use an auto encoder : ) | |
| Mar 18, 2020 at 17:09 | comment | added | formicaman | What if I use OneHotEncoder and then my resulting matrix is not sparse? | |
| Mar 16, 2020 at 18:48 | comment | added | formicaman | Thanks! Any recommendations on how to do the auto-encoding part? | |
| Mar 16, 2020 at 17:37 | history | answered | OmG | CC BY-SA 4.0 |