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Dimensionality Reduction
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…
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Let’s learn about Dimensionality Reduction
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…
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The Art of Dimensionality Reduction
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…
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Reducing Dimensionality from Dimensionality Reduction Techniques
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…
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Understanding Dimensionality Reduction
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…
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Dimensionality Reduction: ways and intuitions
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…
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A Gentle Introduction To Dimensionality Reduction
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…...
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Techniques for Dimensionality Reduction
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…
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Dimensionality Reduction Approaches
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…
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Dimensionality Reduction For Dummies — Part 2: Laying The Bricks
See how your cat can help you understand PCA…
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Dimensionality Reduction For Dummies — Part 3: Connect The Dots
An intuitive solution to PCA using Eigenvalue Decomposition.
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Dimensionality Reduction in Machine Learning
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…
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An Introduction to Dimensionality Reduction
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…
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LDA Is More Effective than PCA for Dimensionality Reduction in Classification Datasets
Linear discriminant analysis (LDA) for dimensionality reduction while maximizing class separability Continue reading on Towards Data Science
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Dimensionality Reduction For Dummies — Part 1: Intuition
Dimensionality Reduction with PCA and SVD. Explained in a simple, visual, and intuitive way. From the big picture to mathematical rigor.
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Dimension Reduction: Facing the Curse of Dimensionality
Comparison of PCA and dynamic factor model Photo by Kolleen Gladden on Unsplash Many data scientists are forced to deal with the challenge of dimension. Data sets can contain huge amounts of variable...
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Dimensionality Reduction by Stochastic Gradient Descent
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…
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Dimensionality Reduction with Python
When building a machine learning model, most likely you will not use all the variables available in your training dataset. In fact, training datasets with hundreds or thousands of features are not…
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Linear Dimensionality Reduction — PCA
previously variety of DRTs were briefly introduced, in this chapter we will go through mathematical working details on one of Linear Dimensionality Reduction Technique and probably most popular one…
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A Complete Guide On Dimensionality Reduction
Do you know, the interest users are generating 2.5 quintillion bytes of data per day? well the data can be from anywhere. Suppose the data collected from Internet based applications are generating…
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Principal Component Analysis for Dimensionality Reduction
Machine Learning is the field where DATA is considered as a boon in the industry. In Machine Learning, having too much data can sometimes also lead to bad results. At a point have more features…
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Dimensionality Reduction in Data Mining
Big data is the large scale of data sets that have multi-level variables and that grow really fast. Volume is the most important aspect of big data. With the recent technological advancements…
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Machine Learning: Dimensionality Reduction via Linear Discriminant Analysis
A machine learning algorithm (such as classification, clustering or regression) uses a training dataset to determine weight factors that can be applied to unseen data for predictive purposes. Before…
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Introduction to Dimensionality Reduction for Machine Learning
Last Updated on June 30, 2020 The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of inpu...
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