mlpack_nmf(1)

💖 mlpack_nmf — non-negative matrix factorization 💖

okay so bestie, this is mlpack_nmf(1), it lives in the User Commands section and it is literally about to change your whole matrix life, no cap ✨

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✨ NAME ✨

mlpack_nmf — non-negative matrix factorization 🦋

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🌸 SYNOPSIS 🌸

mlpack_nmf -i unknown -r int [-q unknown] [-p unknown] [-m int] [-e double] [-s int] [-u string] [-V bool] [-H unknown] [-W unknown] [-h -v]

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💅 DESCRIPTION 💅

so basically, this program performs non-negative matrix factorization on whatever dataset you hand it, and then it stores the resulting decomposed matrices in the files you specify. 🫶

for an input dataset V, NMF decomposes V into two matrices W and H such that

V = W * H

where all elements in W and H are non-negative. periodt, that's the whole vibe.

if V is of size (n x m), then W will be of size (n x r) and H will be of size (r x m), where r is the rank of the factorization — and you specify that with the --rank (-r) parameter. 👑

optionally, the desired update rules for each NMF iteration can be chosen from the following list:

• multdist: multiplicative distance-based update rules (Lee and Seung 1999)
• multdiv: multiplicative divergence-based update rules (Lee and Seung 1999)
• als: alternating least squares update rules (Paatero and Tapper 1994)

the maximum number of iterations is specified with --max_iterations (-m), and the minimum residue required for algorithm termination is specified with the --min_residue (-e) parameter. 📉

for example, to run NMF on the input matrix V.csv using the multdist update rules with a rank-10 decomposition and storing the decomposed matrices into W.csv and H.csv, the following command could be used:

$ mlpack_nmf --input_file V.csv --w_file W.csv --h_file H.csv --rank 10 --update_rules multdist

iconic. ate. 💅

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🎀 REQUIRED INPUT OPTIONS 🎀

these ones are not optional bestie, you genuinely cannot skip them:

• --input_file (-i) [unknown] — the input dataset to perform NMF on. yes queen, give it your data.
• --rank (-r) [int] — the rank of the factorization. 🌟

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⭐ OPTIONAL INPUT OPTIONS ⭐

extra little knobs, take them or leave them:

• --help (-h) [bool] — default help info. 🆘
• --info [string] — print help on a specific option. default value is ''.
• --initial_h_file (-q) [unknown] — initial H matrix.
• --initial_w_file (-p) [unknown] — initial W matrix.
• --max_iterations (-m) [int] — number of iterations before NMF terminates (0 runs until convergence). default value 10000. ⏳
• --min_residue (-e) [double] — the minimum root mean square residue allowed for each iteration, below which the program terminates. default value 1e-05.
• --seed (-s) [int] — random seed. if 0, std::time(NULL) is used. default value 0. 🎲
• --update_rules (-u) [string] — update rules for each iteration; ( multdist | multdiv | als ). default value 'multdist'.
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. 📢
• --version (-V) [bool] — display the version of mlpack. 🌈

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🔥 OPTIONAL OUTPUT OPTIONS 🔥

where your beautiful results go:

• --h_file (-H) [unknown] — matrix to save the calculated H to. 💜
• --w_file (-W) [unknown] — matrix to save the calculated W to. 💖

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🌈 ADDITIONAL INFORMATION 🌈

for further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack. 📚

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ngl bestie, the non-negativity thing is the whole point — don't come at it with negative numbers and then act surprised. 🙈

and that's the whole page, mwah 🧚 this was mlpack-4.8.0, dated 22 June 2026 — mlpack_nmf(1), signing off. slay responsibly 💅✨