Add to ORKGNon-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special case of NMF with matrix L2 norm based error function. In this paper our objective is to analyze the relation between K-means clustering and PLSA by examining the KL-divergence function and matrix L2 norm based error function
Computional learning from multimodal data is often done with matrix factorization techniques such as...
<p><b>Copyright information:</b></p><p>Taken from "LS-NMF: A modified non-negative matrix factorizat...
Nonnegative matrix factorization (NMF) is widely used in a variety of machine learning tasks involv...
Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. I...
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) ...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
• NMF: an unsupervised family of algorithms that simultaneously perform dimension reduction and clus...
NMF and PLSI are two state-of-the-art unsupervised learning models in data mining, and both are wide...
Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been ...
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Vari...
Properties of Nonnegative Matrix Factorization (NMF) as a clustering method are studied by relating ...
This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and...
Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce the dimensi...
Abstract—Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce th...
Abstract Nonnegative Matrix Factorization (NMF) has been proved to be valuable in many ap-plications...
Computional learning from multimodal data is often done with matrix factorization techniques such as...
<p><b>Copyright information:</b></p><p>Taken from "LS-NMF: A modified non-negative matrix factorizat...
Nonnegative matrix factorization (NMF) is widely used in a variety of machine learning tasks involv...
Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. I...
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) ...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
• NMF: an unsupervised family of algorithms that simultaneously perform dimension reduction and clus...
NMF and PLSI are two state-of-the-art unsupervised learning models in data mining, and both are wide...
Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been ...
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Vari...
Properties of Nonnegative Matrix Factorization (NMF) as a clustering method are studied by relating ...
This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and...
Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce the dimensi...
Abstract—Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce th...
Abstract Nonnegative Matrix Factorization (NMF) has been proved to be valuable in many ap-plications...
Computional learning from multimodal data is often done with matrix factorization techniques such as...
<p><b>Copyright information:</b></p><p>Taken from "LS-NMF: A modified non-negative matrix factorizat...
Nonnegative matrix factorization (NMF) is widely used in a variety of machine learning tasks involv...
