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Introduction
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As the world moves forth, algorithms develop with it. The advancement in Artificial Intelligence (AI) has led us to yet another such algorithm that is known as the Artificial Neural Network (ANN). Today, we shall delve into the concept of a type of ANN known as Learning Vector Quantization (LVQ). With time, ANNs have become more popular and their use-cases further spread out. LVQs are a subsequent result of such a trend.
Article Overview
- What is Learning Vector Quantization (LVQ)?
- How does LVQ work?
- Learning Vector Quantization ML Interview Q&A
- Wrap Up
What is Learning Vector Quantization?
Learning Vector Quantization (or LVQ) is a sort of Artificial Neural Network that is based on biological neural system models. It uses a prototype supervised learning classification algorithm and a competitive learning algorithm akin to Self-Organizing Map to train its network. It can also handle the challenge of multiclass classification. The input and output layers are the two layers that make up LVQ.
How does Learning Vector Quantization work?
Let us imagine you have an input data set of size (m, n), with m being the number of training examples and n being the number of features in each sample, and a label vector of size (m, 1 ). It first initializes the size (n, c) weights from the first c number of training examples with various labels and then discards all training samples. The number of classes is denoted by c. Then, the algorithm iterates over the remaining input data, whilst updating the winning vector which is the weight vector with the shortest distance (e.g., Euclidean distance) from each training example for each training example. The trained weights are utilized to classify new examples after the LVQ network has been trained.
Learning Vector Quantization ML Interview Questions/Answers
After having a look at what Learning Vector Quantization is, along with its working, let us have a look at interview questions related to it. It is certainly evident that questions related to are bound to be put forth by your interviewer. Try to answer them in your head before clicking the arrow to reveal the answer.
Its advantages are the following:
- It generates prototypes that are simple to understand by specialists in the application domain.
- LVQ systems can be applied naturally to multiclass classification issues.
- It is in many practical applications such as lossy data compression, lossy data correction, pattern recognition, density estimation, and clustering, among other things.
When it comes to more complex problems, as in when the data has a lot of dimensions or is noisy, the Euclidean distance can undoubtedly prove to be an issue. The feature space must be properly normalized and pre-processed. Even yet, if your data contains a lot of dimensions, you might be suffering from the dimensionality curse and may need to investigate dimensionality reduction.
The steps that are involved in the execution of LVQs are quite straightforward, and after a particular stage, it is all about repeating the same steps till you reach a certain threshold of a result. The steps are as follows:
- Initialization of weights for 1 to n number of epochs
- Selection of a training example
- Computation of the winning vector
- Updation of the winning vector
- Repetition of steps 3, 4, and 5 for all training examples
- Classification of the test sample
It can be defined as a pattern classification process in which each output unit represents a class. The network will be given a set of training patterns with known classifications and an initial distribution of the output class because it employs supervised learning. After the training is complete, LVQ classifies an input vector by assigning it to the same class as the output unit.
Vector quantization is a data compression method that is lossy. It enables the probability density function modeling using the prototype vector distribution. There is some data alteration that causes the compression to be lossy.
Vector Quantization divides an extensive collection of points (vectors) into groups with roughly the same number of points nearest to them. As with k-means and other clustering techniques, each group is represented by its centroid point.
Vector Quantization (VQ) and Self Organizing Map (SOM) are closely related to Learning Vector Quantization (LVQ). However, the name LVQ signifies a class of related algorithms, such as LVQ1, LVQ2, LVQ3, and OLVQ1. That is one difference. Another one is that while VQ and the basic SOM are unsupervised clustering and learning methods, LVQ describes supervised learning. To add further to that, unlike in SOM, no neighborhoods around the “winner” are defined during learning in the basic LVQ, whereby also no spatial order of the codebook vectors is expected to ensue.
Vectors are used in machine learning to represent quantitative, symbolic attributes or numeric characteristics, called features, of an item in a mathematical, easily analyzable form. They are crucial in a variety of machine learning and pattern processing applications. In fact, in many ML models, these feature vectors can be utilized to improve the explainability of said models. This is essential as the world advances further and further, the models turn into much bigger black boxes of complications. Anything that could help ease the understanding of such models is a plus.
Wrap Up
We can see from all the above that Linear Vector Quantization indubitably has its place in the domain of Artificial Neural Networks, and supervised learning in particular. This makes it very much needed in today’s industry when one takes a look at the benefits it provides compared to its drawbacks. Interestingly enough, its link or relation to the usual Vector Quantization or the Self Organizing Map is also of note. The way its algorithm works needs a lot of attention to detail, and not everyone can understand it that easily. Henceforth, it is very worthy of its place in the world of Artificial Intelligence!
