Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
Autor: | Hanspeter Pfister, Nils Gehlenborg, Brant K. Peterson, Eric J. Ma, Fritz Lekschas, Daniel Haehn |
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Rok vydání: | 2021 |
Předmět: |
Computer science
Active learning (machine learning) Nearest neighbor search Relevance feedback 02 engineering and technology Pattern search Article 03 medical and health sciences Pattern detection 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 030304 developmental biology 0303 health sciences business.industry 020207 software engineering Pattern recognition Computer Graphics and Computer-Aided Design Autoencoder Binary classification Active learning 020201 artificial intelligence & image processing Artificial intelligence business Feature learning |
Zdroj: | Computer graphics forum : journal of the European Association for Computer Graphics |
ISSN: | 0167-7055 |
Popis: | We present Peax, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user’s perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users’ perceived similarity. Using an active learning sampling strategy, Peax collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax’s features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques. |
Databáze: | OpenAIRE |
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