GitHub - mchiquier/slsa: Latent semantic analysis. A set of documents are trained, and then a set of documents are individually projected to see which of the Set A documents is the most similar. This version of LSA has a supervised aspect because we know in advance which documents in Set A are most similar to the documents in Set B, and thus in choosing which dimensions to keep we choose those that optimize (minimize) the cosine document vector embeddings between the correct matches.

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Latent semantic analysis. A set of documents are trained, and then a set of documents are individually projected to see which of the Set A documents is the most similar. This version of LSA has a supervised aspect because we know in advance which documents in Set A are most similar to the documents in Set B, and thus in choosing which dimensions…

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Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
19_07_2017.py		19_07_2017.py
New Text Document.txt		New Text Document.txt
class_of_slsa.py		class_of_slsa.py
cursos.py		cursos.py
parseLinkedin.py		parseLinkedin.py
preprocesado.py		preprocesado.py
slsa.py		slsa.py
students.py		students.py