{"id":9066,"date":"2023-02-28T17:51:30","date_gmt":"2023-02-28T12:21:30","guid":{"rendered":"https:\/\/dataaspirant.com\/?p=9066"},"modified":"2023-11-18T14:16:03","modified_gmt":"2023-11-18T08:46:03","slug":"single-value-decomposition-svd","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/single-value-decomposition-svd\/","title":{"rendered":"How to Use Singular Value Decomposition (SVD) In machine Learning"},"content":{"rendered":"
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\"Single

How many times you heard singular value decomposition<\/strong> and skipped the article because of the complex mathematical terms?<\/p>\n

This time you won\u2019t<\/strong>.<\/p><\/blockquote>\n

Singular Value Decomposition, or SVD for short, is a mathematical technique used in machine learning<\/strong><\/a> to make sense of huge and complicated data.<\/p>\n

Let me explain SVD in laymen’s terms:<\/strong><\/p>\n

Imagine you have many different toys you want to organize them. Some are big, some are small, some are red, some are blue, and so on. It can take some work to figure out how to group them together!<\/p>\n

But what if you could break down each toy into its most essential parts?<\/p>\n

For example, you could take a big red ball and break it down into a big part, a red part, and a round part. <\/p>\n

That would make it much easier to compare and group together with other toys that have those same basic parts.<\/p>\n

That’s what SVD does!<\/strong> \ud83d\ude09<\/p><\/blockquote>\n

It takes a big, complicated piece of data and breaks it into its most essential parts. Then we can use those parts to find patterns and similarities in the data<\/strong><\/a>.<\/p>\n

For example: <\/strong><\/p>\n

let’s say you have many pictures of different animals. SVD could break down each picture into its most essential parts, like lines and curves. Then we could use those parts to find patterns, like which animals have similar shapes.<\/p>\n

Before going forward, let’s see the article table of contents.<\/p>\n

How to Use SVD in Machine Learning<\/h2>\n

\"How

Here are a few examples of the types of problems that SVD can help solve:<\/p>\n

Dimensionality Reduction<\/h3>\n

One of the main applications of SVD is to reduce the dimensionality<\/strong> of a dataset. By finding the basic patterns in the data and discarding the less important ones, SVD can help simplify the data and make it easier to work with.<\/p>\n

Data Compression<\/h3>\n

SVD can also compress large datasets<\/strong> without losing too much information. We can represent the data using fewer features by keeping only the most important singular values and associated singular vectors.<\/p>\n

Matrix Approximation<\/h3>\n

Another application of SVD is to approximate a large, complex matrix using a smaller, simpler one. This can be useful when working with large datasets that are difficult to handle directly.<\/p>\n

Collaborative Filtering<\/h3>\n

SVD can be used to predict user preferences in recommender systems<\/strong><\/a> by modeling the relationships between users and items in a large matrix.<\/p>\n

So why there is need for SVD in the first place?<\/strong> <\/p><\/blockquote>\n

Well, sometimes data can be massive and complicated, and it’s hard to make sense of it all. SVD helps us simplify the data and find the most essential parts to understand it better.<\/p>\n

What is Singular Decomposition Value<\/h2>\n

\"What

Singular Value Decomposition is a way to factor a matrix A into three matrices<\/strong>, as follows:<\/p>\n

A = U * S * V^T<\/strong><\/p>\n

Where U and V are orthogonal matrices<\/strong>, and S is a diagonal matrix <\/strong>containing the singular values of A.<\/p>\n

Note:<\/strong> <\/p>\n