mlpack_local_...dinate_coding(1)

💖 mlpack_local_coordinate_coding 💖

hiii bestie 🫶 welcome to the manual page for mlpack_local_coordinate_coding — yes the name is a whole mouthful, the terminal truncates it in the header, we move on. it's giving iconic command, cursed name. let's get into it 💅

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✨ NAME ✨

mlpack_local_coordinate_coding — local coordinate coding

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✨ SYNOPSIS ✨

mlpack_local_coordinate_coding [-k int] [-i unknown] [-m unknown] [-l double] [-n int] [-N bool] [-s int] [-T unknown] [-o double] [-t unknown] [-V bool] [-c unknown] [-d unknown] [-M unknown] [-h -v]

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✨ DESCRIPTION ✨

okay so this is an implementation of Local Coordinate Coding (LCC for short, and yes it's an acronym, we love her). the whole vibe: it codes data that approximately lives on a manifold using a variation of l1-norm regularized sparse coding. it's basically the l1 sparse coding thing but with a glow-up 💖

so picture this. you've got a dense data matrix X with n points and d dimensions. what LCC does is go hunting for:

• a dense dictionary matrix D with k atoms in d dimensions, and
• a coding matrix Z with n points in k dimensions.

and because of the regularization method being used, the atoms inside D should end up lying close to the manifold that your data points live on. that's the whole point, that's the vision, that's the moment 🌸

then, and this is the slay part, your original data matrix X can be reconstructed as D * Z. so basically this program finds a representation of every single point in X as a sparse linear combination of atoms in the dictionary D. representation on representation, periodt.

the coding itself gets found with an algorithm that alternates between two steps — no cap, just those two:

• a dictionary step, which updates the dictionary D
• a coding step, which updates the coding matrix Z

now. to actually run this program, the input matrix X must be specified (with -i), along with the number of atoms in the dictionary (-k). an initial dictionary may also be specified with the '--initial_dictionary_file (-i)' parameter. and the l1-norm regularization parameter is specified with the '--lambda (-l)' parameter.

for example 💫 to run LCC on the dataset data.csv using 200 atoms and an l1-regularization parameter of 0.1, saving the dictionary '--dictionary_file (-d)' and the codes into '--codes_file (-c)', use:

$ mlpack_local_coordinate_coding --training_file data.csv --atoms 200 --lambda 0.1 --dictionary_file dict.csv --codes_file codes.csv

the max number of iterations may be specified with the '--max_iterations (-n)' parameter. and optionally, the input data matrix X can be normalized before coding with the '--normalize (-N)' parameter. lowkey so handy.

an LCC model may be saved using the '--output_model_file (-M)' output parameter. so then, to encode new points from the dataset points.csv with the previously saved model lcc_model.bin, saving the new codes to ’new_codes.csv', you'd run:

$ mlpack_local_coordinate_coding --input_model_file lcc_model.bin --test_file points.csv --codes_file new_codes.csv

and that's it, that's the pipeline, go off 👑

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🌈 OPTIONAL INPUT OPTIONS 🌈

• --atoms (-k) [int] — number of atoms in the dictionary. default value 0.
• --help (-h) [bool] — default help info. it's the "pls explain" button 🥺
• --info [string] — print help on a specific option. default value ''.
• --initial_dictionary_file (-i) [unknown] — optional initial dictionary.
• --input_model_file (-m) [unknown] — input LCC model.
• --lambda (-l) [double] — weighted l1-norm regularization parameter. default value 0.
• --max_iterations (-n) [int] — maximum number of iterations for LCC (0 indicates no limit). default value 0.
• --normalize (-N) [bool] — if set, the input data matrix will be normalized before coding.
• --seed (-s) [int] — random seed. if 0, 'std::time(NULL)' is used. default value 0.
• --test_file (-T) [unknown] — test points to encode.
• --tolerance (-o) [double] — tolerance for objective function. default value 0.01.
• --training_file (-t) [unknown] — matrix of training data (X).
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. the yapper flag fr fr 🗣️
• --version (-V) [bool] — display the version of mlpack.

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🎀 OPTIONAL OUTPUT OPTIONS 🎀

• --codes_file (-c) [unknown] — output codes matrix.
• --dictionary_file (-d) [unknown] — output dictionary matrix.
• --output_model_file (-M) [unknown] — output for trained LCC model.

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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. iykyk 📚

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and that's the whole page bestie, you ate it, no crumbs left 💜

cute lil sign-off moment: this page belongs to mlpack-4.8.0, dated 22 June 2026. remember it, cite it, love it. mwah 🫶✨