Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Document clustering by concept factorization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Independent component analysis for clustering multivariate time series data
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Dynamic Summarization: Another Stride Towards Summarization
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Data Clustering with Semi-binary Nonnegative Matrix Factorization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A sufficient condition for the unique solution of non-negative tensor factorization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Topic-based recommendations in enterprise social media sharing platforms
Proceedings of the fourth ACM conference on Recommender systems
Real-Time speech separation by semi-supervised nonnegative matrix factorization
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Hybrid online non-negative matrix factorization for clustering of documents
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Non-negative multiple matrix factorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A time-based collective factorization for topic discovery and monitoring in news
Proceedings of the 23rd international conference on World wide web
Journal of Global Optimization
Hi-index | 0.00 |
Detecting and tracking latent factors from temporal data is an important task. Most existing algorithms for latent topic detection such as Nonnegative Matrix Factorization (NMF) have been designed for static data. These algorithms are unable to capture the dynamic nature of temporally changing data streams. In this paper, we put forward an online NMF (ONMF) algorithm to detect latent factors and track their evolution while the data evolve. By leveraging the already detected latent factors and the newly arriving data, the latent factors are automatically and incrementally updated to reflect the change of factors. Furthermore, by imposing orthogonality on the detected latent factors, we can not only guarantee the unique solution of NMF but also alleviate the partial-data problem, which may cause NMF to fail when the data are scarce or the distribution is incomplete. Experiments on both synthesized data and real data validate the efficiency and effectiveness of our ONMF algorithm.