Supervised and unsupervised feature learning are two approaches used in machine learning to extract meaningful features from data, but they differ in how they utilize labeled data. Supervised feature learning relies on labeled datasets to guide the learning process, optimizing features to improve predictive accuracy for specific outcomes. In contrast, unsupervised feature learning does not use labels and instead focuses on discovering patterns or structures inherent in the data itself.

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