Learning Vector Quantization

A Learning Vector Quantization is a supervised classification algorithm that uses prototype vectors to classify input data through vector quantization techniques.

• AKA: LVQ Algorithm, LVQ Classification.
• Context:
  • It can typically learn prototype vectors from labeled training data through iterative updates.
  • It can adjust prototype positions by moving them closer to correctly classified samples and away from misclassified ones.
  • It can range from being a Simple LVQ Algorithm to being an Advanced LVQ Algorithm, depending on its update rule complexity.
  • It can support both binary classification tasks and multi class classification tasks.
  • It can maintain a set of prototype vectors for each target class.
  • It can achieve classification performance comparable to other supervised learning algorithms while maintaining interpretable prototypes.
  • ...
• Examples:
  • LVQ Implementations, such as:
    • LVQ1 Algorithm for basic prototype learning.
    • OLVQ1 Algorithm with optimized learning rates.
    • LVQ2 Algorithm with enhanced decision boundary optimization.
    • LVQ3 Algorithm for improved convergence behavior.
  • LVQ Applications, such as:
    • Pattern Recognition Systems using LVQ classifiers.
    • Medical Diagnosis Systems leveraging LVQ classification.
  • ...
• Counter-Examples:
  • K Means Clustering Algorithm, which performs unsupervised learning without using class labels.
  • K Nearest Neighbors Algorithm, which stores all training examples rather than learning prototypes.
• See: Vector Quantization, Prototype Based Learning, Supervised Learning Algorithm, Statistical Classification.

---

---

References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Learning_vector_quantization Retrieved:2015-10-23.
  • In computer science, learning vector quantization (LVQ), is a prototype-based supervised classification algorithm. LVQ is the supervised counterpart of vector quantization systems.

2011

• (Sammut & Webb, 2011) ⇒ Claude Sammut, and Geoffrey I. Webb. (2011). “Learning Vector Quantization.” In: (Sammut & Webb, 2011) p.594

---