An Open Source Pattern Recognition Toolbox for MATLAB

Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of different approaches to a given task. A toolbox with a unified framework across multiple pattern recognition techniques enables algorithm developers the ability to rapidly evaluate different choices prior to deployment. MATLAB is a widely used environment for algorithm development and prototyping, and although several MATLAB toolboxes for pattern recognition are currently available these are either incomplete, expensive, or restrictively licensed. In this work we describe a MATLAB toolbox for pattern recognition and machine learning known as the PRT (Pattern Recognition Toolbox), licensed under the permissive MIT license. The PRT includes many popular techniques for data preprocessing, supervised learning, clustering, regression and feature selection, as well as a methodology for combining these components using a simple, uniform syntax. The resulting algorithms can be evaluated using cross-validation and a variety of scoring metrics to ensure robust performance when the algorithm is deployed. This paper presents an overview of the PRT as well as an example of usage on Fisher’s Iris dataset.

💡 Research Summary

The paper introduces the Pattern Recognition Toolbox (PRT), an open‑source MATLAB toolbox for pattern recognition and machine learning, released under the permissive MIT license. The authors argue that while MATLAB is a dominant environment for algorithm development, existing commercial toolboxes are either incomplete, costly, or have restrictive licenses. PRT addresses this gap by providing a unified, object‑oriented framework that covers data preprocessing, supervised learning, clustering, regression, and feature selection, together with utilities for model composition, cross‑validation, and performance visualization.

The architectural core of PRT consists of three abstract classes: prtDataSet, prtAction, and prtAlgorithm.

• prtDataSet encapsulates feature matrices (X) and target vectors (Y). Subclasses such as prtDataSetClass and prtDataSetRegress distinguish between categorical and continuous targets, enabling type‑specific handling without extra user code.
• prtAction defines the contract for any learning component. Every action must implement a train method, which consumes a prtDataSet and returns a trained action of the same type, and a run method, which maps an input prtDataSet to an output prtDataSet (e.g., predictions, transformed features). This design forces a uniform input‑output signature across all algorithms, making them interchangeable building blocks.
• prtAlgorithm is itself a subclass of prtAction and represents a pipeline of multiple actions. By overloading the + operator for sequential composition and the / operator for parallel composition, users can write expressive, MATLAB‑style expressions such as algo = prtPreProcZmuv + prtPreProcPca('nComponents',2) + prtClassMap;. The sequential operator feeds the output of one action directly into the next, while the parallel operator enables classifier fusion or multi‑branch processing. Because a prtAlgorithm behaves like any other prtAction, it can be trained, run, and cross‑validated with the same API.

PRT ships with a broad set of ready‑to‑use algorithms: support vector machines, relevance vector machines, random forests, partial least squares discriminant analysis, maximum a posteriori classifiers, and many standard preprocessors (zero‑mean/unit‑variance scaling, PCA, filter‑based feature selectors). All these components inherit from prtAction, so extending the toolbox simply requires implementing the two mandatory methods; the rest of the infrastructure (cross‑validation, scoring, plotting) works out‑of‑the‑box.

Model evaluation is tightly integrated. The kfolds function accepts any prtAction or prtAlgorithm and performs k‑fold cross‑validation, returning a prtDataSet that contains predictions and true labels for each fold. Scoring utilities such as prtScoreRoc compute ROC curves, AUC values, and other metrics directly from this output, while built‑in plotting functions render decision boundaries, data projections, and receiver operating characteristic (ROC) curves. This seamless pipeline—from data loading to performance visualization—greatly accelerates the experimental cycle.

The authors illustrate the toolbox with a classic Iris dataset example. They generate a binary classification problem (setosa vs. non‑setosa), apply zero‑mean/unit‑variance scaling (prtPreProcZmuv), reduce dimensionality with PCA (prtPreProcPca), and then train two classifiers: a maximum‑a‑posteriori map (prtClassMap) and a relevance vector machine (prtClassRvm). Using kfolds with five folds, they obtain cross‑validated predictions, compute ROC curves for both models, and plot the results, demonstrating how a complete workflow can be expressed in fewer than ten lines of MATLAB code.

Beyond functionality, the paper emphasizes the open‑source nature of PRT. The toolbox is hosted on GitHub, includes comprehensive documentation, a quick‑start guide, and a full unit‑test suite, ensuring reproducibility and ease of onboarding. An active discussion forum and blog provide community support and encourage contributions. Compatibility with MATLAB versions from 2008a onward guarantees operation on Windows, Linux/Unix, and macOS platforms, making it accessible to a wide academic and industrial audience.

Limitations are acknowledged. Since MATLAB itself is proprietary, the toolbox cannot be used in environments that forbid commercial software. The current algorithm set focuses on classical machine‑learning methods; integration with modern deep‑learning frameworks would require additional wrappers. Performance on very large datasets may be constrained by MATLAB’s interpreted execution model, suggesting that PRT is best suited for prototyping, teaching, and moderate‑scale research rather than production‑level big‑data pipelines.

In summary, the Pattern Recognition Toolbox delivers a coherent, extensible, and license‑friendly solution for MATLAB users who need to experiment with a variety of pattern‑recognition techniques. Its object‑oriented design, operator‑based pipeline composition, and built‑in validation/visualization tools streamline the development cycle, promote reproducible research, and lower the barrier for community‑driven enhancements. For rapid algorithm exploration and educational purposes, PRT stands out as a valuable addition to the MATLAB ecosystem.