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Github Siribatchu Dimensionality Reduction

Leo Migdal
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github siribatchu dimensionality reduction

It is a technique used in machine learning and data science to reduce the number of input variables (features) in a dataset while retaining as much meaningful information as possible. It is essential when dealing with high-dimensional data (i.e., when there are many features) to improve computational efficiency, reduce overfitting, and visualize data more easily. a) Image data set Colab Link: https://colab.research.google.com/drive/1IVwTCiaOXS87xwHSCdG1tbBNZ1fyoIxa#scrollTo=8Ojlz34ENO0I b) Tabular data set Colab Link: https://colab.research.google.com/drive/14Dt3Kyuo_3HQ_hMjC5sX_ORTwixLp9p9#scrollTo=WlS0w3p7oTwK c) Datbricks Colab Link : https://colab.research.google.com/drive/1X0jWRiOoJpiK4KiGW6JAtACdjSL-gmDv Youtube link: https://youtu.be/ilbMDqOuZX8

🍷 Exploring Wine Dataset with Dimensionality Reduction & K-NN I recently worked on the Wine dataset, which has 178 samples and 13 chemical features, and explored how dimensionality reduction techniques can help visualize and... Highlights: K-NN Classification: Achieved high accuracy on original features. Dimensionality Reduction: PCA: Linear reduction to 2D. t-SNE: Non-linear projection showing clear cluster separation. UMAP: Balances local/global structure; n_neighbors affects cluster tightness. Visualizations: Decision boundaries, scatter plots, and cluster centers.

Key Insight: Dimensionality reduction makes it easier to understand and visualize high-dimensional data, revealing patterns that improve interpretability of models. 📌 GitHub: [https://lnkd.in/emQtVn6q] Sometimes when we are working with large datasets with many features, it can be difficult to figure out which features are important and which are not. This is especially true in unsupervised learning problems where we have a dataset \(\mathbf{x}\), but no regression or classification target value \(y\). Fortunately, there are some powerful dimensionality analysis and reduction techniques that can be applied for these problems. Here, we will focus on the most popular of these techniques, principal component analysis (PCA).

In order to identify and extract meaningful features from data, we must first understand how the data is distributed. If the data is standardized (i.e. the transformation \(\mathbf{x} \rightarrow \mathbf{z}\) is applied), then every feature has mean \(\mu = 0\) and standard deviation \(\sigma = 1\); however, significant correlations may still exist between features, making the inclusion of some... We can see the degree to which any pair of standardized features are correlated by examining the entries of the correlation matrix \(\bar{\Sigma}\), given by: where \(\mathbf{z}_1, \mathbf{z}_2, .., \mathbf{z}_N\) is the standardized dataset. As a motivating example, let’s examine the correlation matrix of random 3D points that are approximately confined to the plane defined by the equation \(x_3 = 3x_1 -2x_2\).

We can generate this dataset using the following Python code: The covariance matrix \(\Sigma\) is different from the correlation matrix \(\bar{\Sigma}\), though the two are commonly confused with one another. Both matrices are symmetric with entries given by: A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. In machine learning, the performance of a model only benefits from more features up until a certain point.

The more features are fed into a model, the more the dimensionality of the data increases. As the dimensionality increases, overfitting becomes more likely. There are multiple techniques that can be used to fight overfitting, but dimensionality reduction is one of the most effective techniques. Dimensionality reduction selects the most important components of the feature space, preserving them and dropping the other components. There are a few reasons that dimensionality reduction is used in machine learning: to combat computational cost, to control overfitting, and to visualize and help interpret high dimensional data sets. Often in machine learning, the more features that are present in the dataset the better a classifier can learn.

However, more features also means a higher computational cost. Not only can high dimensionality lead to long training times, more features often lead to an algorithm overfitting as it tries to create a model that explains all the features in the data. Because dimensionality reduction reduces the overall number of features, it can reduce the computational demands associated with training a model but also helps combat overfitting by keeping the features that will be fed to... Included with.css-t3io8q{-webkit-align-items:baseline;-webkit-box-align:baseline;-ms-flex-align:baseline;align-items:baseline;background-color:rgba(255, 255, 255, 0.01);border-radius:4px;-webkit-box-decoration-break:clone;box-decoration-break:clone;color:var(--wf-text--link, #0065D1);display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;font-family:Studio-Feixen-Sans,Arial,sans-serif;font-size:inherit;font-weight:800;line-height:inherit;outline:0;-webkit-text-decoration:underline;text-decoration:underline;text-decoration-color:transparent;text-decoration-thickness:1.25px;-webkit-transition:box-shadow 125ms ease-out,background-color 125ms ease-out,text-decoration-color 125ms ease-out;transition:box-shadow 125ms ease-out,background-color 125ms ease-out,text-decoration-color 125ms ease-out;}.css-t3io8q:hover{background-color:var(--wf-bg--hover, rgba(48, 57, 105, 0.06));}.css-t3io8q:hover{box-shadow:0 0 0 2px var(--wf-bg--hover, rgba(48, 57, 105, 0.06));text-decoration-color:var(--wf-text--link, #0065D1);}Premium or Teams There was an error while loading. Please reload this page.

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It is a technique used in machine learning and data science to reduce the number of input variables (features) in a dataset while retaining as much meaningful information as possible. It is essential when dealing with high-dimensional data (i.e., when there are many features) to improve computational efficiency, reduce overfitting, and visualize data more easily. a) Image data set Colab Link: http...

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Key Insight: Dimensionality reduction makes it easier to understand and visualize high-dimensional data, revealing patterns that improve interpretability of models. 📌 GitHub: [https://lnkd.in/emQtVn6q] Sometimes when we are working with large datasets with many features, it can be difficult to figure out which features are important and which are not. This is especially true in unsupervised learn...

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We Can Generate This Dataset Using The Following Python Code:

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