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What are the real-world applications of Principal Component Analysis?

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Principal Component Analysis, or PCA, is a popular technique for reducing the complexity and dimensionality of large and high-dimensional datasets. By transforming the original variables into a smaller set of new variables, called principal components, that capture most of the variation and information in the data, PCA can help you simplify your analysis, visualize your data, and identify hidden patterns. But how can you use PCA in real-world scenarios? In this article, we will explore some of the common and interesting applications of PCA in different domains and fields.

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1 Data Compression

One of the most obvious and useful applications of PCA is data compression. Data compression is the process of reducing the size of a data set by removing redundant or irrelevant information, while preserving the essential features and quality of the data. Data compression can help you save storage space, improve performance, and speed up processing and transmission of data. PCA can help you achieve data compression by selecting the principal components that explain most of the variance in the data, and discarding the ones that contribute little or nothing. For example, you can use PCA to compress images by reducing the number of pixels or colors, while maintaining the main characteristics and details of the image.

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2 Feature Extraction

Another common and important application of PCA is feature extraction. Feature extraction is the process of transforming or extracting a set of new features from the original data that are more relevant, meaningful, and informative for a specific task or goal. Feature extraction can help you improve the accuracy, efficiency, and interpretability of your data analysis, especially for machine learning and predictive modeling. PCA can help you perform feature extraction by creating principal components that are linear combinations of the original variables, and that are orthogonal and uncorrelated to each other. For example, you can use PCA to extract features that capture the main differences and similarities among groups of customers, products, or markets.

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3 Noise Reduction

A less obvious but equally valuable application of PCA is noise reduction. Noise reduction is the process of removing or reducing the unwanted or random variation or error in the data that can affect the quality and reliability of your data analysis. Noise reduction can help you enhance the signal-to-noise ratio, and reveal the true underlying structure and patterns in the data. PCA can help you achieve noise reduction by filtering out the principal components that have low variance and high noise, and retaining the ones that have high variance and low noise. For example, you can use PCA to reduce noise in speech or audio signals by eliminating the components that are irrelevant or insignificant for the sound quality.

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Eren Unlu

Co-Founder
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At the end of the day PCA and factorization is a compact latent representation method just like autoencoders. The chosen principal components capture the most explicative portions of the data matrix from a linear algebraic perspective.

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4 Data Visualization

A more fun and creative application of PCA is data visualization. Data visualization is the process of presenting and communicating your data in a graphical or visual form that can help you explore, understand, and share your data insights. Data visualization can help you discover new patterns, trends, and relationships in your data, and make your data analysis more engaging and accessible. PCA can help you create data visualizations by reducing the dimensionality of your data to two or three dimensions, which can be easily plotted on a screen or a paper. For example, you can use PCA to visualize complex and high-dimensional data sets such as gene expression, text documents, or social networks.

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5 Anomaly Detection

A more advanced and challenging application of PCA is anomaly detection. Anomaly detection is the process of identifying and flagging the data points or observations that deviate significantly from the normal or expected behavior or pattern in the data. Anomaly detection can help you monitor, detect, and prevent potential problems, risks, or opportunities in your data, such as fraud, errors, defects, or outliers. PCA can help you perform anomaly detection by measuring the distance or deviation of each data point from the principal components, and finding the ones that have unusually large or small values. For example, you can use PCA to detect anomalies in credit card transactions, sensor readings, or network traffic.

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Eren Unlu

Co-Founder
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As like any lossy latent representation learning method, like deep learning autoencoders, PCA can be used for anamoly detection. There are 2 main methods : 1. You can reproject the input like an autoencoder output and compute errors. Error is correlated to anomaly 2. You can try to detect outliers on latent space (on component scores)

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the real-world applications of Principal Component Analysis?

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1 Data Compression

2 Feature Extraction

3 Noise Reduction

4 Data Visualization

5 Anomaly Detection

6 Here’s what else to consider

Statistics

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What are the real-world applications of Principal Component Analysis?

1

2

3

4

5

6

1 Data Compression

2 Feature Extraction

3 Noise Reduction

4 Data Visualization

5 Anomaly Detection

6 Here’s what else to consider

Statistics

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