nmf_projection — Logtalk 3.100.1

# `nmf_projection`

This library implements Non-negative Matrix Factorization (NMF) for continuous datasets whose attribute values are all non-negative. It learns a non-negative basis using deterministic multiplicative updates and represents each transformed instance as a list of non-negative component weights.

## API documentation

Open the ../../apis/library_index.html#nmf_projection link in a web browser.

## Loading

To load all entities in this library, load the `loader.lgt` file:

    | ?- logtalk_load(nmf_projection(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(nmf_projection(tester)).

## Features

- Implements deterministic multiplicative-update training for NMF.

- Requires all training and inference attribute values to be non-negative.

- Supports exporting learned reducers and reloading them for later use.

- Records diagnostics including convergence status, iteration count, final update delta, and reconstruction error.

## Options

- `n_components/1`

  Number of latent components to learn. The current implementation requires the requested count to not exceed the minimum of the sample count and the number of features. Larger requests raise `domain_error(component_count, Requested-Maximum)`.

- `center/1`

  Always `false`. Centering is intentionally disabled because NMF requires non-negative features.

- `feature_scaling/1`

  When `true`, each continuous attribute is divided by its maximum observed training value. When `false`, raw non-negative values are used.

- `maximum_iterations/1`

  Maximum number of multiplicative-update iterations used during both training and per-instance coefficient inference.

- `tolerance/1`

  Stop iteration when the maximum absolute update delta is less than or equal to this value.

## Usage

Assuming a dataset object `parts_based_measurements` implementing the `dimension_reduction_dataset_protocol` protocol:

    | ?- nmf_projection::learn(parts_based_measurements, DimensionReducer).

    | ?- nmf_projection::learn(parts_based_measurements, DimensionReducer, [n_components(2), feature_scaling(true), maximum_iterations(250), tolerance(1.0e-7)]).

    | ?- nmf_projection::learn(parts_based_measurements, DimensionReducer),
         nmf_projection::transform(DimensionReducer, [f1-3.0, f2-0.0, f3-1.5, f4-0.0], ReducedInstance).

    | ?- nmf_projection::learn(parts_based_measurements, DimensionReducer, [n_components(2)]),
         nmf_projection::export_to_file(parts_based_measurements, DimensionReducer, reducer, 'nmf_reducer.pl').

    | ?- logtalk_load('nmf_reducer.pl'),
         reducer(Reducer),
         nmf_projection::transform(Reducer, [f1-3.0, f2-0.0, f3-1.5, f4-0.0], ReducedInstance).

## Dimension reducer representation

Learned NMF reducers use the representation:

    nmf_reducer(Encoders, Components, Diagnostics)

where `Encoders` stores the continuous attribute encoders used to map instances into a non-negative feature vector, `Components` stores the learned non-negative basis vectors, and `Diagnostics` stores metadata about the learned reducer.

## References

- Daniel D. Lee and H. Sebastian Seung. Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems 13, 2001.