Learning the Parts of Objects by Non-negative Matrix Factorization - Daniel D. Lee & H. Sebastian Seung

"Learning the Parts of Objects by Non-negative Matrix Factorization" by Daniel D. Lee and H. Sebastian Seung is a scientific paper that explores a computational approach to learn the different parts of objects through a mathematical technique called non-negative matrix factorization. It aims to understand how objects can be decomposed into their constituent parts using this method.

FAQ

Q: Who are the authors of the document?
A: Daniel D. Lee & H. Sebastian Seung

Q: What is Non-negative Matrix Factorization?
A: Non-negative Matrix Factorization is a mathematical technique used to decompose a matrix into two lower rank matrices, where all elements are non-negative.

Q: What is the purpose of learning the parts of objects?
A: The purpose is to understand how objects are composed of smaller, more basic parts.

Q: What are some applications of Non-negative Matrix Factorization?
A: Some applications include image processing, topic modeling, and document clustering.