A Guide to Locally Linear Embedding for Dimensionality Reduction

The performance of any machine learning model strongly depends on the quality of the data used to train the model. When the data to train the model is very large, its size needs to be scaled down based on several factors. For this purpose, there are many methods used to reduce the dimensionality of the model. Some of these work in the case of linear while some work when there are non-linear relations between the features in the data. In this article, we are going to discuss locally-linear embedding which is an approach to non-linear dimensionality reduction with neighbourhood preserving embeddings of high dimensional data. The major points to be discussed in this article are listed below. Table of Contents Non-Linear Dimensionality ReductionManifold LearningLocally-Linear Embedding(L