[Machine Learning] Dimension Reduction with Principal Component Analysis (PCA) in python

[nelsonnrl]

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Dimensionality Reduction with Principal Component Analysis in python

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Proposed article introduction

In Machine learning, it is common to come across datasets with hundreds or even thousands of features. Implementing models on such datasets becomes a great challenge in terms of computational cost. Also, models build on high dimensional data space are prone to the problem of the course of dimensionality. To minimize this problem, use a technique known as Dimensionality Reduction.

Dimensionality Reduction involves transforming the feature of a dataset from a high-dimensional space to a low-dimensional space. Some of the techniques that are used in dimensionality reduction include PCA, Linear Discriminant Analysis(LDA), Kernal PCA, Conical Correlation Analysis (CCA) e.t.c. Of all these techniques, PCA is the most used technique in dimension reduction.

In this article, we shall talk about the PCA algorithm. Then, we shall learn how this algorithm maps data from high dimensions, say d, to low dimensions, say k, where ( k < d ). Finally, we shall develop a PCA algorithm in python.

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Key takeaways

1. Learners will understand how to develop the PCA algorithm.
2. Learners will understand how they can use approach the problem of dimension reduction using PCA.
3. Learners will understand how to implement the PCA algorithm in python using high dimensional datasets.

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[ahmadmardeni1]

Good afternoon and thank you for submitting your topic suggestion. Your topic form has been entered into our queue and should be reviewed (for approval) as soon as a content moderator is finished reviewing the ones in the queue before it.

[ahmadmardeni1]

Does your article overlap with the following articles? @nelsonnrl

• Face Recognition using Principal Component Analysis (PCA)
• Image Compression using Principal Component Analysis (PCA)

[nelsonnrl]

@ahmadmardeni1 I have gone through the above two articles. For both articles, PCA is implemented on unstructured data, i.e., the image dataset. In my article, we will implement PCA on structured data, i.e., the dataset of the database. The article on Face Recognition using Principal Component Analysis (PCA) implementation is made in Matlab. Also, in the second article, implementation is made in python, but still, the difference arises because we are dealing with different types of datasets. The only overlap is that the article on Image Compression using Principal Component Analysis (PCA) covers the intuition part I intended to dive into. Since this resource is already in place, I think learners can be referred to this resource and my article base only on how to implement PCA on a structured dataset in python. So, there's an outstanding feature between my article and the above two articles.

[ahmadmardeni1]

Sounds like a helpful topic - let's please be sure it adds value beyond what is in any official docs and/or what is covered in other blog sites. (the articles should go beyond a basic explanation - and it is always best to reference any EngEd article and build upon it). @nelsonnrl

Please be attentive to grammar/readability and make sure that you put your article through a thorough editing review prior to submitting it for final approval. (There are some great free tools that we reference in EngEd resources.) ANY ARTICLE SUBMITTED WITH GLARING ERRORS WILL BE IMMEDIATELY CLOSED.

Please be sure to double-check that it does not overlap with any existing EngEd articles, articles on other blog sites, or any incoming EngEd topic suggestions (if you haven't already) to avoid any potential article closure, please reference any relevant EngEd articles in yours. - Approved