LF-LDA: A Supervised Topic Model for Multi-Label Documents Classification.
Yongjun ZhangZijian WangYongtao YuBolun ChenJialin MaLiang ShiPublished in: Int. J. Data Warehous. Min. (2018)
Keyphrases
- topic models
- multi label
- statistical topic models
- topic modeling
- text classification
- latent dirichlet allocation
- multi label classification
- text documents
- latent topics
- topic discovery
- image classification
- class labels
- document classification
- text categorization
- lda model
- text mining
- probabilistic topic models
- feature selection
- machine learning
- supervised learning
- unsupervised learning
- image annotation
- classification algorithm
- probabilistic model
- text classifiers
- bag of words
- classification accuracy
- news articles
- binary classification
- generative model
- decision trees
- max margin
- feature extraction
- graph cuts
- co occurrence
- latent variables
- author topic model
- naive bayes
- training samples
- semi supervised learning
- semi supervised
- learning algorithm
- information retrieval
- support vector machine svm
- support vector machine
- active learning
- natural language
- training data
- image segmentation