1W-MINDS: February 16th, Nicolas Gillis: Nonnegative Matrix Factorization: Introduction, denti... скачать в хорошем качестве

1W-MINDS: February 16th, Nicolas Gillis: Nonnegative Matrix Factorization: Introduction, denti...

Given a nonnegative matrix X and a factorization rank r, nonnegative matrix factorization (NMF) approximates the matrix X as the product of a nonnegative matrix W with r columns and a nonnegative matrix H with r rows. NMF has become a standard linear dimensionality reduction technique in data mining and machine learning. In this talk, we first introduce NMF and show how it can be used as an interpretable unsupervised data analysis tool in various applications, including hyperspectral image unmixing, image feature extraction, and document classification. Then, we discuss the issue of non-uniqueness of NMF decompositions, also known as the identifiability issue, which is crucial in many applications. Finally, we discuss how we can go beyond NMF by considering non-linear and deep extensions which are useful in real-world applications and offer many venues for future research. Information about the seminar, upcoming talks, a link for signing up for announcements and links to videos of other talks can be found on the web page: https://sites.google.com/view/minds-s...