Non-negative matrix factorization code

In summary, non-negative matrix factorization code is a mathematical algorithm used for data analysis and machine learning that decomposes a matrix into two non-negative matrices to reduce dimensionality while preserving important features. The algorithm iteratively updates the matrices to minimize the difference between the original matrix and their product. It has many benefits, such as identifying patterns, improving interpretability, and compressing data, and can be applied in various fields such as image and video processing, text mining, and bioinformatics. However, it also has limitations, including sensitivity to initialization, outliers, and overfitting, and may not work well with sparse or noisy data.
  • #1
buupq
6
1
Hello,

I'm looking for the non-negative matrix factorization (NNMF) source code. I checked several linear algebra libraries (e.g., LaPack, mkl), but it seems that this subroutine is not available. Does anyone know where I can find this source code?

https://en.wikipedia.org/wiki/Non-negative_matrix_factorization#cite_note-23

Thank you!
 
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  • #2
Did you try googling "non-negative matrix factorization code"? The first hit is:
https://www.csie.ntu.edu.tw/~cjlin/nmf/
which contains a Matlab implementation as well as one in Python, Go, and C.
 

What is non-negative matrix factorization code?

Non-negative matrix factorization code refers to a mathematical algorithm used in data analysis and machine learning. It aims to decompose a given matrix into two non-negative matrices, with the goal of reducing the dimensionality of the data while preserving important features.

How does non-negative matrix factorization code work?

The algorithm works by iteratively updating the two non-negative matrices until they converge to a solution that best represents the original matrix. This is achieved by minimizing the difference between the original matrix and the product of the two non-negative matrices.

What are the benefits of using non-negative matrix factorization code?

Non-negative matrix factorization code has several benefits, including reducing the dimensionality of data, identifying underlying patterns and features, and improving the interpretability of data. It is also useful for data compression and feature extraction in various applications.

What are the applications of non-negative matrix factorization code?

Non-negative matrix factorization code has many applications, including image and video processing, text mining, recommendation systems, and bioinformatics. It is also used in data clustering, classification, and dimensionality reduction in various fields such as computer vision, natural language processing, and genomics.

What are the limitations of non-negative matrix factorization code?

Non-negative matrix factorization code has some limitations, including sensitivity to initialization, the presence of outliers in the data, and the potential for overfitting. It also requires choosing the appropriate number of components, which can be challenging in some cases. Additionally, it may not work well with highly sparse or noisy data.

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