Feature Extraction: How to Make Data Processing Easier

Feature extraction pulls the most helpful information from a large amount of data. It helps you make sense of overwhelming raw data that can be tricky to work with, especially in machine learning applications. 

Say you’re analyzing pictures of dogs and cats. Feature extraction identifies patterns like fur texture or ear shape to help you differentiate between the two. It’s a critical process in image recognition.

Image recognition software uses feature extraction to identify and isolate relevant parts of an image so computers understand it more easily. This allows the software to quickly and accurately recognize objects in an image. 

The feature extraction process can be done manually or automatically. A decent understanding of the background or domain helps you extract germane features if you go for the manual option. 

Automated feature extraction uses deep networks or special algorithms to cull pertinent components without human intervention. It allows you to develop machine learning models quickly.

Importance of feature extraction

Feature extraction enables image and speech recognition, predictive modeling, and natural language processing (NLP). In these applications, raw data contains a multitude of irrelevant or redundant features that make data processing tricky.

Extraction reduces data complexity (aka data dimensionality). It might involve creating new features or manipulating data to separate relevant and irrelevant ones. 

Extracted characteristics facilitate the creation of more informative datasets used in classification, prediction, and clustering. 

Feature extraction techniques

Below are some techniques data scientists use to extract features from raw data. Consider two factors when choosing your technique: information loss and computational complexity.

Unfortunately, there’s always a chance of losing essential data during the extraction process. Moreover, some approaches can be cost-intensive for large datasets.  

Statistical methods

Statistical methods summarize and explain data patterns in the feature extraction process. 

Its common attributes are mean, median, standard deviation, covariance and correlation, and regression analysis. These models report trends, spread, and links within a data collection. 

Feature extraction from textual data

Feature extraction techniques work to transform unorganized textual data into numerical formats suitable for use in machine learning models. It’s an important technique for NLP, and comprises two methods: 

• The bag of words (BoW) model is a basic text extraction method. It maintains word frequency while ignoring structure or sequence. This method is helpful in document classification, where each word is taken as a feature to train the classifier. 
• Term frequency-inverse document frequency (TF-IDF) finds problems that aren’t common in the overall collection of datasets. It’s an extension of BoW, which considers not only the frequency of words in a single document but all other documents in the corpus. It determines a word’s value based on its frequency in the document and its rarity across the entire body of work. Data scientists use TF-IDF in text classification, information retrieval, and mood analysis. 

Dimensionality reduction methods

The feature extraction methods discussed here reduce data complexity and improve interpretability. They include several approaches, such as linear discriminant analysis (LDA), principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE). 

• Principal component analysis selects variables in data that account for the most variation and uses them to convert high-dimensional data to lower-dimensional data. As an unsupervised method, it doesn’t consider class identifiers. 
• Linear discriminant analysis (LDA) identifies linear characteristic combinations to distinguish between two object classes. Unlike PCA, LDA, a supervised method, takes class labels into account. 
• T-distributed stochastic neighbor embedding (t-SNE) uses a nonlinear approach to lower data's dimensionality while still retaining its local structure. It embeds high-dimensional data in 2D or 3D space. This method works well for complex datasets. 
• Autoencoders consist of an encoder and a decoder. The encoder maps raw data to a lower-dimensional version, also called latent space. The decoder maps the latent space back to the original raw data. They create a compact data representation for anomaly detection, generative modeling, and dimensionality reduction. It trains neural networks to recreate input, discovering features in data. Through these processes, the dimensionality is reduced while significant features from data are successfully extracted. 
• Independent component analysis (ICA) combines related data characteristics to minimize dimensionality. It divides a multivariate signal into additive independent subcomponents. 

Feature extraction from signals

There are two methods to extract features from signals, including: 

• A fourier transform converts a signal from the time or space domain and represents it in the frequency domain. It analyzes the signal's feature components.  
• The wavelet transform represents a signal in both the time and frequency domain. It helps analyze signals whose frequencies vary over time. 

Feature extraction from images

Different techniques detect features such as edges, shapes, and motion in a digital image. Below are a few notable feature extraction techniques for images. 

• Convolutional neural networks (CNN): Features extracted from deep layers of CNN facilitate several computer vision tasks, such as object detection and image classification. 
• Scale-invariant feature transform (SIFT): This method extracts unchanging features from images that remain reliable at any scale or rotation change, including modifications in lighting setup. It’s largely used in tasks like object detection. 
• Histogram of oriented gradients (HOG): This technique is used for object detection and task recognition. It computes how intensity gradients and edge directions are distributed in an image. 

Feature extraction use cases

Below are some common use cases of feature extraction in machine learning applications. 

• Transfer learning. ML models learn about the specific datasets they’re trained on. Suppose the model’s dataset comprises English essays; the model will automatically learn the basics of English grammar. When training a new model, the same feature of the model can be transferred to it. This process is known as transfer learning.  

• Retrieval, reranking, and retrieval augmented generation. In NLP, retrieval systems pull from an extensive data corpus to find information or documents to respond to search queries. Reranking improves the quality of results by reordering the outcomes based on relevance to the query. Feature extraction models that cater to retrieval and reranking assist in retrieval-augmented generation. Here, user inputs first pass through a knowledge base of a generative model. Relevant information is taken from there to augment the prompt. This reduces hallucinations in generations.  

Tools and libraries for feature extraction

Below are some popular tools and libraries that cater to feature extraction. 

• OpenCV, a computer vision library, offers multiple image feature extraction techniques, such as SIFT, speeded-up robust features (SURF), and oriented FAST and rotated BRIEF (ORB).
• Scikit-learn is a Python library with feature extraction techniques such as principal component analysis and independent component analysis. 
• TensorFlow/Keras are Python’s deep learning libraries that supply users with application programming interfaces (APIs) for creating and training neural networks. 
• Librosa’s Python library contributes tools for feature extraction from audio signals. 
• PyTorch is similar to TensorFlow. It supports building customer neural network architectures that assist feature extraction processes. 
• Natural Language Toolkit (NLTK) is a Python library with tools for NLP tasks and feature extraction techniques from text data, such as BoW and TF-IDF. 
• Matrix Laboratory (MATLAB) has image and signal processing tools, including feature extraction techniques like wavelet and Fourier transforms. 
• Gensim provides tools for NLP tasks like topic similarity and document modeling. It’s another Python library that offers feature extraction tools from text data. 

Make sense of raw data

Feature extraction helps discover meaningful information from raw data. With this, it has become a crucial process for applications like image recognition and text analysis. Choose your technique wisely to receive the more accurate results.

Learn more about how feature extraction makes deep learning models effective in object classification and computer vision.