Unsupervised feature extraction and supervised feature extraction differ in terms of the availability of labeled data during the process.

  Unsupervised feature extraction is a technique used when there is no prior knowledge or labeled data available for the task at hand. It aims to automatically discover patterns, structures, and relationships within the data without any external supervision. This is typically done through methods such as clustering, dimensionality reduction, or generative models. Unsupervised feature extraction methods focus on finding inherent patterns within the data itself without any guidance from pre-labeled examples.

  On the other hand, supervised feature extraction requires labeled data, where each instance of data is associated with a class or target value. In this approach, the features are extracted in such a way that they are representative of the underlying patterns and can effectively discriminate between different classes or categories. Supervised feature extraction methods often rely on techniques like linear discriminant analysis (LDA), support vector machines (SVM), or deep learning. The goal is to identify features that capture relevant information related to the target variable.

  In summary, the key difference between unsupervised and supervised feature extraction lies in the availability of labeled data. Unsupervised methods seek to uncover patterns without any external guidance, while supervised methods utilize labeled data to extract features that are more discriminative for the task at hand.