What is Feature Extraction? Feature Extraction explained

Feature Extraction is a technique created to help reduce the dimensionality of data, often used in high-dimensional data sets that consume a lot of computational costs. This technique is applied to many different machine learning algorithms.

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What is Feature Engineering?

First, you need to understand the concept of Feature Engineering. According to research, this is the process of converting the original raw data set into a set of attributes. These properties can help represent the original data set better, facilitate easier problem-solving, and better compatibility with each machine learning model.

This process is often divided into three main stages:

• Feature Extraction: This is an automated process used to reduce data dimensionality so that the original data is converted into a simpler and smaller form before being included in the prediction model.
• Feature Selection: This is the process of solving problems in improving the accuracy of an algorithm by automatically selecting a subset of initial features so that these selected features are suitable for the problem at hand.
• Feature Construction: This is the process of building new features, a job that requires a lot of creativity, and time, because each different type of data will have different ways to be built.

What is Feature Extraction?

From what we learned earlier, we know that Feature Extraction is a small part of a larger technique called Feature Engineering. This is a very important technique because it helps reduce data dimensionality, allowing input variables to be selected or combined into predictive features while keeping the original data intact.

This technique has three ways to do it:

• Autoencoder: A technique that automatically encodes input data from a high-dimensional space to a low-dimensional space and then decodes back from a low-dimensional space to a high-dimensional space so that the output information of the decoding process and inputs must be approximately equal.
• Bag-of-Words: A bag-of-words algorithm commonly used in Natural Language Processing (NLP), allows them to extract information from text segments by building a bag of words and finding ways to encode text content. into a vector of word frequencies without regard to word order and grammatical structure.
• Image Processing: These are algorithms used to detect features on images. These algorithms can be manual feature extraction methods on images or using feature extractors through CNN.

Feature Extraction for text

Feature Extraction for text is a relatively complicated process because text data can exist in many different forms, such as lowercase letters, uppercase letters, punctuation, and special characters,… Besides, different languages also have different character patterns and different grammatical structures.

From here, the question is how to encode characters into numbers. The answer is to divide the text into its smallest units and build an indexing dictionary for these units. To do this, we have two ways:

• Encoding by word: With this method, the words in the sentence will be the smallest unit. When done in this way, the size of the dictionary will be very large depending on the number of different words appearing in all the sentences. document.
• Encoding by character: With this method, we will use the symbols in the alphabet to make a word encoding dictionary. When done in this way, the size of the dictionary will be much smaller.

The main methods based on the above two methods that have been used at present are bag-of-words, bag-of-n-gram, and TF-IDF.

Feature Extraction for images

Feature extraction for images is no less complex than for text. Previously, this technique was also performed manually through algorithms such as HOG, SHIFT,… This algorithm has many disadvantages plus a large amount of Big Data, making model training and prediction slower.

At present, as CNN networks grow stronger, we are gradually switching to newer end-to-end architectures. From here, it is not necessary to initialize manually but can be generated randomly according to assumed distributions.

From here we can easily extract all information from images such as text, time, geography,… and use them for specific applications such as Midjourney AI Art, NightCafe,…

Conclusion

Finally, we have succeeded in sharing with you the most comprehensive and easy-to-understand information for the Feature Extraction concept. Hope we have helped you understand more about this concept and see you again in other content from AZcoin.