Data Dimensionality Reduction and Feature Extraction with PHP

Machine learning plays an increasingly important role in modern technology. As data volumes grow, reducing dimensionality and extracting key features become essential for efficient model training and prediction. This article introduces how to perform data dimensionality reduction and feature extraction using PHP, providing complete code examples.

1. What are Data Dimensionality Reduction and Feature Extraction?

In machine learning, dimensionality reduction transforms high‑dimensional data into a lower‑dimensional space while preserving essential information, helping to lower computational complexity and improve visualisation. Feature extraction selects the most representative and influential attributes from raw data, reducing dataset size and enhancing model training efficiency.

2. Using PHP for Data Dimensionality Reduction and Feature Extraction

PHP can leverage machine‑learning libraries such as PHP‑ML to carry out these tasks. The following sections demonstrate the process using the PCA algorithm as an example.

1. Install a PHP Machine Learning Library

First, install the PHP‑ML library via Composer:

composer require php-ai/php-ml

2. Data Preparation and Preprocessing

Prepare the dataset and apply standardisation and imputation before reduction:

use Phpml\Dataset\CsvDataset;
use Phpml\Preprocessing\Imputer;
use Phpml\Preprocessing\StandardScaler;

$dataset = new CsvDataset('data.csv', $numFeatures = null, $delimiter = ',', $skipHeader = true);
$imputer = new Imputer();
$imputer->fit($dataset->getSamples());
$imputer->transform($dataset->getSamples());

$scaler = new StandardScaler();
$scaler->fit($dataset->getSamples());
$scaler->transform($dataset->getSamples());

3. Perform Dimensionality Reduction with PCA

Apply Principal Component Analysis (PCA) to reduce the data to two dimensions:

use Phpml\DimensionalityReduction\PCA;

$pca = new PCA(2);
$pca->fit($dataset->getSamples());
$pca->transform($dataset->getSamples());

4. Feature Extraction

Extract informative features using tokenisation, stop‑word removal and TF‑IDF transformation:

use Phpml\FeatureExtraction\StopWords;
use Phpml\FeatureExtraction\TokenCountVectorizer;
use Phpml\FeatureExtraction\TfIdfTransformer;

$vectorizer = new TokenCountVectorizer(new StopWords('en'));
$vectorizer->fit($samples);
$vectorizer->transform($samples);

$transformer = new TfIdfTransformer();
$transformer->fit($samples);
$transformer->transform($samples);

Conclusion

Dimensionality reduction and feature extraction are vital techniques in machine learning that help compress data size while retaining critical information, leading to more efficient training and higher prediction accuracy. The PHP examples above illustrate how to apply these techniques using the PHP‑ML library, enabling better handling of large datasets.