dimensionality Reduction

Towards Data Science

Dimensionality reduction aims to preserve as much information as possible from higher dimensional vectors. Principal Component Analysis (PCA) [1] and T-Distributed Stochastic Neighbouring Entities…

Dimensionality Reduction in Machine Learning

Analytics Vidhya

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some…

5 Must-Know Dimensionality Reduction Techniques via Prince

Towards Data Science

According to Wikipedia, Dimensionality Reduction is a transformation of High-Dimensionality space data into a Low-dimensionality space. In other words, Dimensionality Reduction transforms data from a…...

An Introduction to Dimensionality Reduction

Towards Data Science

In statistics, machine learning, and information theory, dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal…

4 ways to Reduce Dimensionality of Data

Towards Data Science

Dimensionality Reduction is the process of reducing the number of features or variables in the dataset. It is the transformation of data from a high-dimensional space into a low-dimensional space so…

Dimensionality Reduction by Stochastic Gradient Descent

Analytics Vidhya

Dimensionality reduction is the process of reducing a potentially large set of features F to a smaller set of features F’ to be considered in a given machine learning or statistics problem. In an…

Dimensionality-Reduction with Latent Dirichlet Allocation

Towards Data Science

Dimensionality-reduction is an unsupervised machine learning technique that is often used in conjunction with supervised models. While reducing the dimensionality often makes a feature-based model…

A Guide to Dimensionality Reduction in Python

Towards Data Science

Dimensionality reduction is the process of transforming high-dimensional data into a lower dimensional format while preserving the most important properties. This technique has applications in many…

11 Different Uses of Dimensionality Reduction

Towards Data Science

Dimensionality is the number of variables in your data. Dimensionality reduction is the process of reducing the number of variables in the input data. It has 11 different uses.

What does dimensionality reduction do, really?

Towards Data Science

An outline of what dimensionality reduction does at a high level, and an animation to illustrate the process.

Dimensionality Reduction: PCA versus Autoencoders

Towards Data Science

Dimensionality reduction is a technique of reducing the feature space to obtain a stable and statistically sound machine learning model avoiding the Curse of dimensionality. There are mainly two…

BPDR: A New Dimensionality Reduction Technique

Towards Data Science

Dimensionality reduction algorithms such as LDA, PCA, or t-SNE are great tools to analyze unlabeled (or labeled) data and gain more information about its structure and patterns. Dimensionality…