On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
GTM: the generative topographic mapping
Neural Computation
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Parametric Embedding for Class Visualization
Neural Computation
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On class visualisation for high dimensional data: exploring scientific data sets
DS'06 Proceedings of the 9th international conference on Discovery Science
FpViz: a visualizer for frequent pattern mining
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Interactive, topic-based visual text summarization and analysis
Proceedings of the 18th ACM conference on Information and knowledge management
FpVAT: a visual analytic tool for supporting frequent pattern mining
ACM SIGKDD Explorations Newsletter
CloseViz: visualizing useful patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
TIARA: a visual exploratory text analytic system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online multiscale dynamic topic models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic models with power-law using Pitman-Yor process
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Development of a curriculum analysis tool
ITHET'10 Proceedings of the 9th international conference on Information technology based higher education and training
Modeling multiple users' purchase over a single account for collaborative filtering
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Sequential Modeling of Topic Dynamics with Multiple Timescales
ACM Transactions on Knowledge Discovery from Data (TKDD)
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
TIARA: Interactive, Topic-Based Visual Text Summarization and Analysis
ACM Transactions on Intelligent Systems and Technology (TIST)
TopicViz: interactive topic exploration in document collections
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Combining supervised and unsupervised models via unconstrained probabilistic embedding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A study on document retrieval system based on visualization to manage OCR documents
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
Combining supervised and unsupervised models via unconstrained probabilistic embedding
Information Sciences: an International Journal
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We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-dimensional scaling, we consider a mapping from the visualization space into the space of documents as a generative process of documents. In the model, both documents and topics are assumed to have latent coordinates in a two- or three-dimensional Euclidean space, or visualization space. The topic proportions of a document are determined by the distances between the document and the topics in the visualization space, and each word is drawn from one of the topics according to its topic proportions. A visualization, i.e. latent coordinates of documents, can be obtained by fitting the model to a given set of documents using the EM algorithm, resulting in documents with similar topics being embedded close together. We demonstrate the effectiveness of the proposed model by visualizing document and movie data sets, and quantitatively compare it with conventional visualization methods.