Timeline for How to perform SVD to impute missing values, a concrete example
Current License: CC BY-SA 4.0
13 events
| when toggle format | what | by | license | comment | |
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| S Dec 19, 2019 at 15:01 | history | suggested | Steffen Moritz | CC BY-SA 4.0 |
Removed additional comments
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| Dec 19, 2019 at 8:57 | review | Suggested edits | |||
| S Dec 19, 2019 at 15:01 | |||||
| S Dec 11, 2019 at 17:30 | history | suggested | Steffen Moritz | CC BY-SA 4.0 |
Removed additional comments
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| Dec 11, 2019 at 16:51 | review | Suggested edits | |||
| S Dec 11, 2019 at 17:30 | |||||
| May 13, 2019 at 0:35 | answer | added | Ambareesh | timeline score: 4 | |
| Apr 27, 2018 at 13:44 | comment | added | Geoffrey Anderson | Everyone did not watch at least one movie, right? So removing all users who have missing data will result in zero users, and zero rows in your utility (rating) matrix. So you cannot remove any rows that are missing some data, right? SVD is not helpful for datasets with missing values. There are other matrix factorization techniques however which can impute them. Look, SVD would need you to impute missing data in advance, some other way. You can do imputation the silly way by just using any old constant but then what is the point of using such garbage data? Do you want garbage to be output? | |
| Apr 27, 2018 at 13:28 | answer | added | Geoffrey Anderson | timeline score: 2 | |
| Apr 18, 2017 at 22:10 | vote | accept | Boro Dega | ||
| S Apr 18, 2017 at 7:23 | history | suggested | farmer | CC BY-SA 3.0 |
Fixed grammar. Capitalized SVD. Make links informative but still short.
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| Apr 18, 2017 at 6:54 | review | Suggested edits | |||
| S Apr 18, 2017 at 7:23 | |||||
| May 27, 2016 at 10:12 | answer | added | sascha | timeline score: 8 | |
| May 27, 2016 at 9:03 | history | edited | amoeba | CC BY-SA 3.0 |
edited tags; edited title
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| May 27, 2016 at 0:32 | history | asked | Boro Dega | CC BY-SA 3.0 |