Timeline for Is non-negative matrix factorization for machine learning obsolete?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Jan 6, 2022 at 11:20 | answer | added | Ashwiniku918 | timeline score: 1 | |
| Jan 22, 2021 at 21:10 | comment | added | nbro | @RamiZK The related Wikipedia page contains a section Applications that provides an answer to your own question. After reading it and consulting the cited resources there, you could try to provide an answer below to your own question (if no one else attempts to do it) ;) By the way, welcome to AI SE! Take at a look at ai.stackexchange.com/help/on-topic to know more about our scope, if you have some time. | |
| Jan 22, 2021 at 21:07 | history | edited | nbro | CC BY-SA 4.0 |
added 5 characters in body; edited tags; edited title
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| Jan 22, 2021 at 15:38 | comment | added | Rami ZK | I know for sure that matrix factorisation is revolutionary in other fields and calculations. My first intuition is that by using matrix factorization in ML application is similar to finding patterns in the data. And therefore why bother using for such tasks as long as we know that neural networks can perform way better. Thanks for your comment. | |
| Jan 22, 2021 at 13:30 | comment | added | user9947 | Matrix factorisation does have a lot of uses. Don't count it out. Although its use lies in Image processing applications and probably highly theoretical ML applications, inbetween whatever happens is not well understood or formulated. (I am assuming you are talking about SVD, LU, QR, ED, etc, etc.). The inequalities/things you can produce by these methods are simply unbelievably easy as compared to any other Analysis. (From my very limited experience) | |
| Jan 22, 2021 at 10:09 | review | First posts | |||
| Jan 22, 2021 at 16:02 | |||||
| Jan 22, 2021 at 10:04 | history | asked | Rami ZK | CC BY-SA 4.0 |