dimensionality reduction Archives

Singular Value Decomposition for Dimensionality Reduction

Singular Value Decomposition usually referred to as SVD, is a type of matrix decomposition technique that is popularly used to reduce the dimensions of the data. SVD decomposes a mxn real matrix A into a product […]

Why High Dimensional Data are a Curse?

In the era of big data, massive datasets(not only in the number of samples being collected but also in the number of features) are increasingly prevalent in many disciplines and are often difficult to interpret. In […]

Types of PCA

In the era of big data, massive datasets are increasingly common in many disciplines and are often difficult to interpret. In this article, we’ll discuss the principal component analysis which is widely used as a dimensionaity […]

Introduction to Principal Component Analysis(PCA)

Principal Component Analysis is an unsupervised dimensionality reduction technique, which is extensively used in machine learning. It helps us to alleviate the problem of the curse of dimensionality by reducing the dimension of the data. PCA […]

Introduction to T-SNE with implementation in python

INTRODUCTION to T – SNE: T-SNE is a non-linear dimensionality reduction technique used to visualize high-dimensional data in two or more dimensions. Unlike PCA which preserves only the global structure of the data T-SNE preserves both the local […]