FeatureExtractor

FeatureExtractor is a component or process in machine learning that converts raw input data into a numeric feature vector that captures information relevant for predictive tasks. It can be implemented as handcrafted feature extraction, where domain knowledge defines measurements, or as learned extraction, where a model automatically derives representations from data. In a typical workflow, raw data passes through a feature extractor to produce a representation that a downstream model uses for classification, regression, or clustering.

In different domains, feature extractors take various forms. In computer vision, common hand-engineered features include SIFT, SURF, and HOG, while in audio processing, methods such as MFCCs are used. In natural language processing, TF-IDF vectors or bag-of-words representations function as feature extractors. With deep learning, features are often the activations of intermediate layers of a pre-trained network, providing rich, learned representations that can be reused for related tasks through transfer learning.

Key considerations in designing or selecting a feature extractor include invariance to nuisance variations, robustness to noise, dimensionality of the resulting vector, computational cost, and compatibility with the downstream model. Feature extraction can also be paired with dimensionality reduction techniques to reduce storage and improve generalization. Evaluating a feature extractor typically involves assessing downstream task performance, generalization to new data, and efficiency.

In software libraries, a feature extractor often exposes a transform or forward operation that maps inputs to feature vectors and may be integrated into pipelines alongside preprocessing steps and learning algorithms. Related concepts include feature engineering and transfer learning.