How to Supervise Topic Models
| Autor: | Cheng Zhang, Hedvig Kjellström |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Topic model
Topic Modeling LDA Computer science business.industry SIGNAL (programming language) SLDA Factorized Supervised Topic Models Machine learning computer.software_genre Latent Dirichlet allocation Synthetic data symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Datorseende och robotik (autonoma system) symbols Noise (video) Artificial intelligence business computer Computer Vision and Robotics (Autonomous Systems) |
| Zdroj: | Computer Vision-ECCV 2014 Workshops ISBN: 9783319161808 ECCV Workshops (2) |
| DOI: | 10.1007/978-3-319-16181-5_39 |
| Popis: | Supervised topic models are important machine learning tools whichhave been widely used in computer vision as well as in other domains. However,there is a gap in the understanding of the supervision impact on the model. Inthis paper, we present a thorough analysis on the behaviour of supervised topicmodels using Supervised Latent Dirichlet Allocation (SLDA) and propose twofactorized supervised topic models, which factorize the topics into signal andnoise. Experimental results on both synthetic data and real-world data for computer vision tasks show that supervision need to be boosted to be effective andfactorized topic models are able to enhance the performance. QC 20141024 |
| Databáze: | OpenAIRE |
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