165
Voted
SLSFS
2005
Springer
15 years 11 months ago
2005
Springer
Abstract. Matrix factorization is a fundamental building block in many computer vision and machine learning algorithms. In this work we focus on the problem of ”structure from mo...
137
Voted
ECML
2005
Springer
15 years 11 months ago
2005
Springer
Abstract. In this paper, dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linea...
167
Voted
WEBI
2007
Springer
15 years 12 months ago
2007
Springer
Nonnegative Matrix Factorization (NMF) has been proven to be effective in text mining. However, since NMF is a well-known unsupervised components analysis technique, the existing ...
144
click to vote
ICDM
2007
IEEE
16 years 4 days ago
2007
IEEE
An interesting problem in Nonnegative Matrix Factorization (NMF) is to factorize the matrix X which is of some specific class, for example, binary matrix. In this paper, we exten...
150
Voted
ICDM
2008
IEEE
16 years 7 days ago
2008
IEEE
Recently Non-negative Matrix Factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-nega...
171
click to vote
SIGIR
2009
ACM
16 years 9 days ago
2009
ACM
With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in com...
181
click to vote
RECSYS
2009
ACM
16 years 9 days ago
2009
ACM
In the Netflix Prize competition many new collaborative filtering (CF) approaches emerged, which are excellent in optimizing the RMSE of the predictions. Matrix factorization (M...
154
Voted
ICANN
2009
Springer
16 years 11 days ago
2009
Springer
Abstract. This paper’s intention is to present a new approach for decomposing motion trajectories. The proposed algorithm is based on nonnegative matrix factorization, which is a...
181
click to vote
ECWEB
2009
Springer
16 years 11 days ago
2009
Springer
Abstract. Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both implicit and explicit feedback based recommender systems. We show that by using...
165
click to vote
WWW
2010
ACM
16 years 24 days ago
2010
ACM
The Web abounds with dyadic data that keeps increasing by every single second. Previous work has repeatedly shown the usefulness of extracting the interaction structure inside dya...