Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
| العنوان: | Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model |
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| المؤلفون: | Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil Lawrence |
| المصدر: | University of Bristol-PURE |
| مصطلحات موضوعية: | Computer Science::Machine Learning, Condensed Matter::Quantum Gases, FOS: Computer and information sciences, Computer Science - Learning, ComputingMethodologies_PATTERNRECOGNITION, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG) |
| الوصف: | Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we propose a new kernel formulation that enables the separation of latent space and derives an efficient variational inference method. The performance of our model is demonstrated with an imbalanced medical image dataset. Comment: ICLR 2016 Workshop |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a4a725417ddf575da4a80dcbc82bdfb https://research-information.bris.ac.uk/en/publications/2dc8a117-f4df-48fc-a72e-3a916350d978 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....8a4a725417ddf575da4a80dcbc82bdfb |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |