The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …
methods result in misleading results and waste of computing resources due to lack of timely …
Recommender systems clustering using Bayesian non negative matrix factorization
Recommender Systems present a high-level of sparsity in their ratings matrices. The
collaborative filtering sparse data makes it difficult to: 1) compare elements using memory …
collaborative filtering sparse data makes it difficult to: 1) compare elements using memory …
Fusion of different height pyroelectric infrared sensors for person identification
J **ong, F Li, J Liu - IEEE Sensors Journal, 2015 - ieeexplore.ieee.org
Due to the instability and poor identification ability of a single pyroelectric infrared (PIR)
detector for human target identification, this paper presents a PIR detection identification …
detector for human target identification, this paper presents a PIR detection identification …
Uncertainty modeling and price-based demand response scheme design in smart grid
Transforming conventional passive customers into active participants who interact with the
utility in real time is the key idea of demand response (DR) in smart grid. However, an …
utility in real time is the key idea of demand response (DR) in smart grid. However, an …
Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior
Non-negative matrix factorization (NMF) is generally an ill-posed problem which requires
further regularization. Regularization of NMF using the assumption of sparsity is common as …
further regularization. Regularization of NMF using the assumption of sparsity is common as …
Supervised cross-modal factor analysis for multiple modal data classification
In this paper we study the problem of learning from multiple modal data for purpose of
document classification. In this problem, each document is composed two different modals of …
document classification. In this problem, each document is composed two different modals of …
Regularized maximum correntropy machine
In this paper we investigate the usage of regularized correntropy framework for learning of
classifiers from noisy labels. The class label predictors learned by minimizing transitional …
classifiers from noisy labels. The class label predictors learned by minimizing transitional …
A new recommendation approach based on probabilistic soft clustering methods: A scientific documentation case study
Recommender system (RS) clustering is an important issue, both for the improvement of the
collaborative filtering (CF) accuracy and to obtain analytical information from their high …
collaborative filtering (CF) accuracy and to obtain analytical information from their high …
Stochastic behavior of the nonnegative least mean fourth algorithm for stationary Gaussian inputs and slow learning
Some system identification problems impose nonnegativity constraints on the parameters to
be estimated due to inherent physical characteristics of the unknown system. The …
be estimated due to inherent physical characteristics of the unknown system. The …
Exponential total variation model for noise removal, its numerical algorithms and applications
C Sun, C Tang, X Zhu, H Ren - AEU-International Journal of Electronics …, 2015 - Elsevier
The total variation model has been considered to be one of the most successful and
representative denoising models that can preserve edges well. However, its main shortage …
representative denoising models that can preserve edges well. However, its main shortage …