Exploring the roles of supervised and unsupervised learning in vector search

Supervised learning is a method in ML where a model learns from labeled data to determine a mapping between input features and output labels. During the training process, the model adjusts its parameters to minimize the error between its predictions and the true labels. Supervised learning is widely used in various applications, such as image classification, speech recognition, and natural language processing (NLP). One of the key challenges with supervised learning is that a curated labeled dataset must be provided to train the model.

Unsupervised learning, on the other hand, involves discovering patterns and structures in input data without using labeled examples. This type of learning focuses on finding similarities, differences, or underlying structures within the data by using techniques such as clustering, dimensionality reduction, and density estimation. Unsupervised learning is commonly applied...