dimensionality reduction

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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…

Let’s learn about Dimensionality Reduction

Towards AI

For example:- We have data in spreadsheet format and we have vast amounts of variables (age, name, sex, Id, and so on..). In a simple way “The number of input variables or features for a dataset is…

The Art of Dimensionality Reduction

Analytics Vidhya

Suppose you want to solve a predictive modeling problem, and for the same, you start to collect data. You would never know what exact features you want and how much data is needed. Hence, you go for…

Reducing Dimensionality from Dimensionality Reduction Techniques

Towards Data Science

In this post I will do my best to demystify three dimensionality reduction techniques; PCA, t-SNE and Auto Encoders. My main motivation for doing so is that mostly these methods are treated as black…

Understanding Dimensionality Reduction

Towards AI

We all understand that more data means better AI. That sounds great! But, with the recent blast of information, we often end in a problem of too much data! We need all that data. But it turns out to…

Dimensionality Reduction: ways and intuitions

Towards Data Science

After Big data applications became pervasive, the curse of dimensionality turns out to be more serious than expected. As a result, visualization and analysis became harder for this high dimensional…

A Gentle Introduction To Dimensionality Reduction

Towards Data Science

As the word exploratory suggest, Exploratory Factor Analysis (EFA) is that preliminary examination seeking to understand relationship between variables. When first exposed to the data, the researcher…...

Techniques for Dimensionality Reduction

Towards Data Science

Currently, we’re on the edge of a wonderful revolution: Artificial Intelligence. In addition to this, the recent ‘Big Bang’ in large datasets across companies, organisation, and government…

Dimensionality Reduction Approaches

Towards Data Science

The full explosion of big data has persuaded us that there is more to it. While it is true, of course, that a large amount of training data allows the machine learning model to learn more rules and…

Dimensionality Reduction For Dummies — Part 2: Laying The Bricks

Towards Data Science

See how your cat can help you understand PCA…

Dimensionality Reduction For Dummies — Part 3: Connect The Dots

Towards Data Science

An intuitive solution to PCA using Eigenvalue Decomposition.

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…