kernel_pca_projection — Logtalk 3.100.1

# `kernel_pca_projection`

Kernel Principal Component Analysis reducer for continuous datasets. The library implements the `dimension_reducer_protocol` defined in the `dimension_reduction_protocols` library and learns a nonlinear projection by centering the training data, optionally standardizing continuous attributes, building a centered kernel Gram matrix, and extracting deterministic principal directions in sample space using portable power iteration with deflation.

## API documentation

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

## Loading

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

    | ?- logtalk_load(kernel_pca_projection(loader)).

## Testing

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

    | ?- logtalk_load(kernel_pca_projection(tester)).

## Features

- Continuous Datasets: Accepts datasets containing only continuous attributes. Missing or nonnumeric values are rejected.

- Centering and Optional Scaling: Centers all attributes and optionally standardizes them before evaluating kernels.

- Configurable Shortfall Handling: Lets callers choose whether a component-extraction shortfall raises an error or truncates the learned reducer with explicit diagnostics.

- Supported Kernels: Supports linear, polynomial with non-negative offset, and radial basis function kernels through the `kernel/1` option.

- Shared Gram-Centering Helpers: Delegates training Gram-matrix centering and out-of-sample Gram-vector centering to the shared `linear_algebra` library.

- Portable Eigensolver: Uses deterministic power iteration with deflation instead of backend-specific linear algebra libraries.

- Projection API: Transforms a new instance into a list of `component_N-Value` pairs using centered kernel evaluations against the training rows.

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

## Options

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

- `n_components/1`: Number of kernel principal components to extract. Requests that exceed `SampleCount - 1` raise `domain_error(component_count, Requested-Maximum)`. The default is `2`.

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

- `shortfall_policy/1`: Controls what happens when the centered kernel Gram matrix yields fewer numerically significant components than requested. Options: `truncate` (default), which returns a reducer with fewer components and records a `shortfall(truncated(Requested, Learned, ResidualEigenvalue, Tolerance))` diagnostic, or `error`, which raises `domain_error(component_count, Requested-Learned)`.

- `kernel/1`: Kernel specification. Supported values are `linear` (default), `polynomial(Degree, Gamma, Coef0)` with positive `Degree`, positive `Gamma`, and non-negative `Coef0`, and `rbf(Gamma)` with positive `Gamma`.

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

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

## Usage

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

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

### Learning a reducer

    | ?- kernel_pca_projection::learn(correlated_plane, DimensionReducer).

    | ?- kernel_pca_projection::learn(correlated_plane, DimensionReducer, [n_components(1), kernel(rbf(0.25))]).

### Transforming new instances

    | ?- kernel_pca_projection::learn(correlated_plane, DimensionReducer, [n_components(2), kernel(rbf(0.25))]),
         kernel_pca_projection::transform(DimensionReducer, [x-2.0, y-4.0, z-6.0], ReducedInstance).

### Exporting and reusing the reducer

    | ?- kernel_pca_projection::learn(correlated_plane, DimensionReducer, [n_components(1)]),
         kernel_pca_projection::export_to_file(correlated_plane, DimensionReducer, reducer, 'kernel_pca_reducer.pl').

    | ?- logtalk_load('kernel_pca_reducer.pl'),
         reducer(Reducer),
         kernel_pca_projection::transform(Reducer, [x-1.0, y-2.0, z-3.0], ReducedInstance).

## Dimension reducer representation

The learned dimension reducer is represented by a compound term with the functor chosen by the implementation and arity 7. For example:

    kernel_pca_reducer(Encoders, TrainingRows, RowMeans, TotalMean, Components, ExplainedVariances, Diagnostics)

Where:

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

- `TrainingRows`: Encoded training rows used when evaluating kernels for new instances.

- `RowMeans`: Per-training-row kernel means used for centering out-of-sample kernel vectors.

- `TotalMean`: Global kernel mean used for centering both the training Gram matrix and new kernel vectors.

- `Components`: List of normalized dual projection vectors in descending variance order.

- `ExplainedVariances`: List of kernel Gram matrix eigenvalues matching the extracted components.

- `Diagnostics`: Learned metadata including the effective training options, kernel preprocessing, sample count, explained variances, and optional truncate-mode shortfall 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. Schölkopf, B., Smola, A., and Müller, K.-R. (1998) - “Nonlinear component analysis as a kernel eigenvalue problem”.

2. Shawe-Taylor, J. and Cristianini, N. (2004) - “Kernel Methods for Pattern Analysis”.