lda_projection — Logtalk 3.100.1

# `lda_projection`

Linear Discriminant Analysis projection for labeled continuous datasets. Supports continuous attributes only.

The library implements the `dimension_reducer_protocol` defined in the `dimension_reduction_protocols` library and learns discriminant directions by centering the training data, optionally standardizing continuous attributes, building regularized within-class and between-class scatter matrices, whitening the Fisher criterion using a Cholesky factorization, and extracting discriminant directions using deterministic power iteration with deflation.

Requires a dataset implementing `supervised_dimension_reduction_dataset_protocol` and therefore uses class labels during training.

## API documentation

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

## Loading

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

    | ?- logtalk_load(lda_projection(loader)).

## Testing

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

    | ?- logtalk_load(lda_projection(tester)).

## Features

- Supervised Continuous Datasets: Accepts labeled datasets implementing `supervised_dimension_reduction_dataset_protocol` and containing only continuous attributes.

- Centering and Optional Scaling: Centers all attributes and optionally standardizes them before estimating class scatter matrices.

- Portable Fisher Eigensolver: Uses regularized within-class scatter, Cholesky whitening, and deterministic power iteration instead of backend-specific linear algebra libraries.

- Projection API: Transforms a new instance into a list of `component_N-Value` pairs.

- Model Export: Learned reducers can be exported as predicate clauses or written to a file.

- Missing Values: Missing or nonnumeric values are rejected.

## Options

The `learn/3` predicate accepts the following options:

- `n_components/1`: Maximum number of discriminant components to extract. Requests that exceed the minimum of the number of features and `number_of_classes - 1` raise `domain_error(component_count, Requested-Maximum)`. The default is `2`.

- `feature_scaling/1`: Whether to standardize continuous attributes before estimating scatter matrices. Options: `true` (default) or `false`.

- `maximum_iterations/1`: Maximum number of power-iteration steps used when estimating each discriminant direction. The default is `1000`.

- `tolerance/1`: Positive convergence tolerance used both for power-iteration stopping and for deciding when additional discriminant directions are numerically negligible. The default is `1.0e-8`.

- `regularization/1`: Positive diagonal regularization added to the within-class scatter matrix before whitening. The default is `1.0e-6`.

## Usage

The following examples use the sample labeled dataset shipped with the `dimension_reduction_protocols` library:

    | ?- logtalk_load(dimension_reduction_protocols('test_datasets/labeled_measurements')).

### Learning a reducer

    | ?- lda_projection::learn(labeled_measurements, DimensionReducer).

    | ?- lda_projection::learn(labeled_measurements, DimensionReducer, [n_components(1), feature_scaling(false), maximum_iterations(250), tolerance(1.0e-7), regularization(1.0e-5)]).

### Transforming new instances

    | ?- lda_projection::learn(labeled_measurements, DimensionReducer),
         lda_projection::transform(DimensionReducer, [length-5.1, width-3.5, height-1.4, weight-0.2], ReducedInstance).

    | ?- lda_projection::learn(labeled_measurements, DimensionReducer, [n_components(1)]),
         lda_projection::transform(DimensionReducer, [length-6.2, width-3.4, height-5.4, weight-2.3], ReducedInstance).

### Exporting and reusing the reducer

    | ?- lda_projection::learn(labeled_measurements, DimensionReducer, [n_components(1)]),
         lda_projection::export_to_file(labeled_measurements, DimensionReducer, reducer, 'lda_projection_reducer.pl').

    | ?- logtalk_load('lda_projection_reducer.pl'),
         reducer(Reducer),
         lda_projection::transform(Reducer, [length-5.1, width-3.5, height-1.4, weight-0.2], ReducedInstance).

## Dimension reducer representation

The learned dimension reducer is represented by a compound term with the functor chosen by the implementation and arity 4. For example:

    lda_projection_reducer(Encoders, Components, ClassValues, Diagnostics)

Where:

- `Encoders`: List of continuous attribute encoders storing attribute name, mean, and scale.

- `Components`: List of learned discriminant vectors in component order.

- `ClassValues`: Ordered list of class labels used during training.

- `Diagnostics`: Learned reducer metadata including the effective training options and model details.

When exported using `export_to_clauses/4` or `export_to_file/4`, this reducer term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

## References

1. Fisher, R. A. (1936) - “The use of multiple measurements in taxonomic problems”.

2. Rao, C. R. (1948) - “The utilization of multiple measurements in problems of biological classification”.