feature extraction

Feature extraction is a crucial process in machine learning and data analysis that involves identifying and extracting relevant features from raw data. These features, once extracted, are used to create a more informative dataset that can be utilized for various tasks such as classification, prediction, and clustering.

The primary aim of feature extraction is to reduce the complexity of data (often referred to as “data dimensionality”) while retaining as much relevant information as possible. This simplification helps improve the performance and efficiency of machine learning algorithms and makes the analysis process more manageable[4].

Feature extraction techniques vary based on the type of data and the specific application. For instance, in image processing, techniques such as Histogram of Oriented Gradients (HOG), Speeded-Up Robust Features (SURF), and Local Binary Pattern (LBP) features are used. In contrast, for text data, techniques like Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) are popular[1][5].

While automated feature extraction has become more common with the advent of deep learning models like Convolutional Neural Networks (CNNs) for image data, manual feature extraction is not entirely outdated. It still holds value, especially in domains where deep learning models may not readily apply or in cases where domain-specific knowledge can significantly enhance the feature selection process[2].

Citations:

[1] https://www.mathworks.com/discovery/feature-extraction.html

[2] https://datascience.stackexchange.com/questions/62409/is-manual-feature-extraction-outdated

[3] https://www.snowflake.com/guides/feature-extraction-machine-learning/

[4] https://domino.ai/data-science-dictionary/feature-extraction

[5] https://www.geeksforgeeks.org/feature-extraction-techniques-nlp/

[6] https://www.geeksforgeeks.org/feature-extraction-in-data-mining/

[7] https://datascience.stackexchange.com/questions/88162/cannot-understand-feature-extraction

[8] https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be

[9] https://datascience.stackexchange.com/questions/85146/feature-extraction-in-machine-learning

[10] https://deepai.org/machine-learning-glossary-and-terms/feature-extraction

[11] https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/dmcon/feature-extraction.html