Lecture
This lecture covers Latent Semantic Indexing (LSI), a technique to map documents and queries into a lower-dimensional space of higher-level concepts. It explains how LSI uses concepts for retrieval, the concept of dimensionality reduction, and the process of similarity computation in the concept space. The lecture also delves into Singular Value Decomposition (SVD) and its application in constructing the concept space. Additionally, it discusses the implementation of LSI in Python and alternative techniques like Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation. The lecture concludes with the practical use of LSI in unsupervised learning, document organization, retrieval, and classification.