Answers.Body: <p>I will give you a few scenarios where matrix factorisation stills works pretty well.</p>
  <ol>
  <li>Topic Modelling : Given a matrix of Document as Rows and Terms/Words as column you can use Non Negative Matrix factorisation to identify Topics. Number of Topics is defined by user or can be treated as hyperparameter.</li>
  </ol>
  <p><a href="https://i.sstatic.net/mRqFd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/mRqFd.png" alt="enter image description here" /></a></p>
  <p>Image Ref : <a href="https://towardsdatascience.com/nmf-a-visual-explainer-and-python-implementation-7ecdd73491f8" rel="nofollow noreferrer">https://towardsdatascience.com/nmf-a-visual-explainer-and-python-implementation-7ecdd73491f8</a></p>
  <ol start="2">
  <li><p>Collaborative filtering which is a class of recommendation engines used matrix factorisation extensively</p>
  </li>
  <li><p>Matrix factorisation can also be used as a feature reduction for a lot of ML algorithm. Infact most commonly used feature reduction techinue PCA is kind of matrix factorisation</p>
  </li>
  </ol>
  <p>Yes, we may have advanced algorithms which may perform better but dont forget.</p>
  <p>No Free Lunch Theorem in Machine Learning : No ML algorithm will outperform every other algorithms for every use case</p>
  <p>So yes matrix factorisation is still relevant and forms backbone of a lot of advanced ML algotithms</p>
  
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Answers.CreationDate: 2022-01-06T11:20:49.590Z
Answers.LastActivityDate: 2022-01-06T11:20:49.590Z
Answers.UserDisplayName: Ashwiniku918
Body: <p>I am taking a course about using matrix factorization for machine learning.</p>
  <p>The first thing that came into my mind is by using the matrix factorization we are always limited to linear relationships between the data, which is very limiting to predict complex patterns.</p>
  <p>In comparison with neural networks, where we can use a non-linear activation function. It seems to me that all the tasks that matrix factorization can achieve will score better using a simple multilayer neural network.</p>
  <p>So, can I conclude that <a href="https://en.wikipedia.org/wiki/Non-negative_matrix_factorization" rel="nofollow noreferrer">NMF</a> and <a href="https://en.wikipedia.org/wiki/Matrix_decomposition" rel="nofollow noreferrer">matrix factorization</a> for machine learning, in general, are not that practical, or there are cases where it's better to use NMF?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-22T15:38:56.293Z
Comments.Text: 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.
Comments.UserDisplayName: Rami ZK
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-22T21:10:56.450Z
Comments.Text: @RamiZK [The related Wikipedia page](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization) 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 https://ai.stackexchange.com/help/on-topic to know more about our scope, if you have some time.
Comments.UserDisplayName: nbro
Tags: <neural-networks><machine-learning><reference-request><applications>
Title: Is non-negative matrix factorization for machine learning obsolete?
UserDisplayName: Rami ZK
ThreadText: # Is non-negative matrix factorization for machine learning obsolete?
  
  (Asked by: username_0 on 2021-01-22)
  
  I am taking a course about using matrix factorization for machine learning.
  
  The first thing that came into my mind is by using the matrix factorization we are always limited to linear relationships between the data, which is very limiting to predict complex patterns.
  
  In comparison with neural networks, where we can use a non-linear activation function. It seems to me that all the tasks that matrix factorization can achieve will score better using a simple multilayer neural network.
  
  So, can I conclude that [NMF](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization) and [matrix factorization](https://en.wikipedia.org/wiki/Matrix_decomposition) for machine learning, in general, are not that practical, or there are cases where it's better to use NMF?
  
  **Tags:** neural-networks, machine-learning, reference-request, applications
  > **Comments:**
  > **username_0**: 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.  
  > **username_1**: @RamiZK [The related Wikipedia page](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization) 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 https://ai.stackexchange.com/help/on-topic to know more about our scope, if you have some time.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-01-06. Score: 1
  
  I will give you a few scenarios where matrix factorisation stills works pretty well.
  
  1. Topic Modelling : Given a matrix of Document as Rows and Terms/Words as column you can use Non Negative Matrix factorisation to identify Topics. Number of Topics is defined by user or can be treated as hyperparameter.
  
  [![enter image description here](https://i.sstatic.net/mRqFd.png)](https://i.sstatic.net/mRqFd.png)
  
  Image Ref : <https://towardsdatascience.com/nmf-a-visual-explainer-and-python-implementation-7ecdd73491f8>
  
  2. Collaborative filtering which is a class of recommendation engines used matrix factorisation extensively
  3. Matrix factorisation can also be used as a feature reduction for a lot of ML algorithm. Infact most commonly used feature reduction techinue PCA is kind of matrix factorisation
  
  Yes, we may have advanced algorithms which may perform better but dont forget.
  
  No Free Lunch Theorem in Machine Learning : No ML algorithm will outperform every other algorithms for every use case
  
  So yes matrix factorisation is still relevant and forms backbone of a lot of advanced ML algotithms
  
  
  ---
  
  