What is feature extraction?
Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing. A characteristic of these large data sets is a large number of variables that require a lot of computing resources to process. Feature extraction is the name for methods that select and /or combine variables into features, effectively reducing the amount of data that must be processed, while still accurately and completely describing the original data set.
Feature extraction essentially is the process of converting raw data into numerical features that can be processed while preserving the information in the original data set. It yields better results than if you directly apply machine learning techniques on the raw data.
With the ascent of deep learning, feature extraction has been replaced to a great extent by the first layers of deep networks – but primarily for image data. For signal and time-series applications, feature extraction remains the first challenge that needs a significant amount expertise before you can get around to building effective predictive models.
How can feature extraction be carried out?
Feature extraction can be carried out either manually or automatically.
Manual feature extraction needs you to identify and describe the features that are relevant for a specific problem and implement a way to extract those features. In several situations, possessing a good understanding of the background or domain can help in making informed decisions regarding which features could turn out to be useful. Over decades of research, engineers and scientists have developed feature extraction methods for images, signals, and text. An example of a simple feature is the mean of a window in a signal.
Automated feature extraction involves making use of specialized algorithms or deep networks for the purpose of extracting features automatically from signals or images without the need for human intervention. This technique can be particularly useful when you wish to move quickly from raw data to developing machine learning algorithms. Wavelet scattering is an example of automated feature extraction.
Why is feature extraction used?
The process of feature extraction is useful when you need to reduce the number of resources needed for processing without losing important or relevant information. Feature extraction can also reduce the amount of redundant data for a given analysis. Also, the reduction of the data and the machine’s efforts in building variable combinations (features) facilitate the speed of learning and generalization steps in the machine learning process, thus proving:
- Accuracy improvements
- Overfitting risk reduction
- Speed up in training
- Improved Data Visualization
- Increase in explainability of our model
Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features). These new reduced set of features should then be able to summarize most of the information contained in the original set of features. In this way, a summarised version of the original features can be created from a combination of the original set. Feature extraction reduces the amount of redundant data in the dataset, which helps build the model with less machine effort and even increases the speed of learning and generalization steps in the machine learning process.
What are the applications of feature extraction?
The purpose of autoencoders is unsupervised learning of efficient data coding. Feature extraction is used here to identify key features in the data for coding by learning from the coding of the original data set to derive new ones.
Bag-of-words is a technique for natural language processing that extracts the words (features) used in a sentence, document, website, etc. and classifies them by frequency of use. This technique can also be applied to image processing.
3. Image Processing
Algorithms are used to detect features such as shaped, edges, or motion in a digital image or video.
How is feature extraction performed for image data?
Feature extraction for image data involves representing the interesting parts of an image in the form of a compact feature vector. In the past, this was carried out with specialized feature detection, feature extraction, and feature matching algorithms. Today, deep learning is prevalent in image and video analysis, and has become known for its ability to use raw image data as input, skipping the feature extraction step. Regardless of which approach you use, computer vision applications like image registration, object detection and classification, and content-based image retrieval, all need effective representation of image features. This could be either done implicitly by the first layers of a deep network, or explicitly applying some of the longstanding image feature extraction techniques.
What is the difference between feature selection and feature extraction?
Feature selection is used for the purpose of filtering irrelevant or redundant features from your dataset. The main difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.
How is feature extraction performed on signal features and time-frequency transformations?
Feature extraction will identify the most discriminating characteristics in signals, which a machine learning or a deep learning algorithm can more easily consume. Training machine learning or deep learning directly with raw signals often yields poor results because of the high data rate and information redundancy.
You can apply pulse and transition metrics, measure signal-to-noise ratio (SNR), estimate spectral entropy and kurtosis, and compute power spectra when analyzing signals and sensor data.
You can use time-frequency transformations, like the short-time Fourier transform (STFT) as signal representations for training data in machine learning and deep learning models. As an example, convolutional neural networks (CNNs) are often used on image data and can successfully learn from the 2D signal representations returned by time-frequency transformations.
You can also use other time-frequency transformations, depending on the specific application or the characteristics. As an example, the constant-Q transform (CQT) offers a logarithmically spaced frequency distribution; the continuous wavelet transform (CWT) is usually effective at identifying short transients in non-stationary signals.