Dimensionality reduction coupled with outlier detection is a technique used to reduce the complexity of high-dimensional data while identifying outliers or extremes in the data. The goal is to identify patterns and relationships within the data while minimizing the influence of noise and outliers.
Dimensional reduction techniques such as principal component analysis (PCA) and t-SNE can transform high-dimensional data into a lower dimensional space while preserving the most important information. External detection algorithms can then be applied to the lower-dimensional data to identify extreme values that may indicate errors, anomalies, or interesting patterns.
Dimensionality reduction combined with anomaly detection has applications in finance, medicine, image processing, and natural language processing. It can be used to identify fraudulent transactions in finance, detect anomalies in patient data in medicine, identify unusual patterns in images in image processing, and identify unusual patterns in textual data such as spam emails and sentiment analysis in natural language processing.
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Recently, a research team from the USA published a paper on the effectiveness of lower-dimensional anomaly detection techniques and the accuracy of dimensionality reduction techniques in identifying outliers. The goal is to understand how much data can be visualized while maintaining external characteristics.
The main idea of the paper is to investigate the effect of dimensionality reduction on the accuracy of outgoing detection techniques. The authors aim to explore the extent to which outliers can be accurately identified while reducing the dimensionality of the data. They use several commonly used dimension reduction techniques and external detection methods to test their hypothesis on different real data sets. The paper’s contribution is to provide empirical evidence of the effectiveness of outlier detection techniques in lower dimensions and the role of dimensionality reduction in preserving intrinsic properties of outliers.
In this pilot study, the authors explored different dimensional minimization techniques and their ability to detect outliers in high-dimensional datasets. The authors performed experiments on 18 different datasets and compared the results of external detection using various methods, including Forest Isolation, PCA, UMAP, and Angle Based Detective Detection (ABOD). The study found that Isolation Forest and PCA were the best methods for detecting outliers, with Isolation Forest having fewer errors when using PCA to reduce dimensions. The study also investigated the effect of adding an extra dimension of Euclidean distances to the data set, which increased the number of true outliers detected. LOF was the best method for detecting true outliers compared to ABOD and Isolation Forest. However, the study concluded that the method did not induce quality but increased the number of true outliers often correctly detected. The study provides scatter plots and a bar chart to illustrate the results of the experiments.
This study examined the relationship between dimensionality reduction and outlier detection by evaluating several standard outlier detection techniques on different datasets using common dimensionality reduction techniques. The results show that while the stability of the outlier detection techniques may decrease in low-dimensional spaces, their ability to find true outliers often improves. However, the study was limited to numerical data and was experimental only. In the future, the researchers plan to explore this problem theoretically and extend their study to include categorical and mixed data. They also plan to investigate the use of state-of-the-art anomaly detection techniques to identify outliers and use dimensionality reduction to visualize and explain them.
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Mahmoud is a PhD researcher in machine learning. He also carries a
Bachelor’s degree in Physical Sciences and Master’s degree in
Communication systems and networks. its current fields
The research is concerned with computer vision, stock market forecasting and deep
to learn. Produced many scholarly articles on person re
Determination and study of depth stability and robustness
networks.
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