Unsupervised vector quantization

1. Is LVQ unsupervised?
2. What are the main differences between SOM and LVQ models?
3. What do you mean by vector quantization?
4. What are the types of learning vector quantization?

Is LVQ unsupervised?

In computer science, learning vector quantization (LVQ) is a prototype-based supervised classification algorithm. LVQ is the supervised counterpart of vector quantization systems.

What are the main differences between SOM and LVQ models?

Self organizing maps are more suited for clustering(dimension reduction) rather than classification. But SOM's are used in Linear vector quantization for fine tuning. But LVQ is a supervised leaning method. So to use SOM's in LVQ, LVQ should be provided with a labelled training data set.

What do you mean by vector quantization?

Vector quantization (VQ) is an efficient coding technique to quantize signal vectors. It has been widely used in signal and image processing, such as pattern recognition and speech and image coding.

What are the types of learning vector quantization?

Learning Vector Quantization ( or LVQ ) is a type of Artificial Neural Network which also inspired by biological models of neural systems. It is based on prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm similar to Self Organizing Map.