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.

中文翻译：

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P eax : 使用无监督深度表示学习在序列数据中进行交互式视觉模式搜索

我们提出了 Peax，这是一种基于特征的新型技术，用于在序列数据（如时间序列或映射到基因组序列的数据）中进行交互式视觉模式搜索。由于搜索空间大、模式的视觉复杂性以及用户对相似性的感知，通过相似性在视觉上搜索模式通常具有挑战性。例如，在基因组学中，研究人员试图将多变量序列数据中的模式与细胞或致病过程联系起来，但缺乏基本事实和高方差使得自动模式检测不可靠。我们开发了一种卷积自动编码器，用于序列数据中区域的无监督表示学习，与现有的相似性度量相比，它可以捕获复杂模式的更多视觉细节。使用这个学习到的表示作为序列数据的特征，我们随附的视觉查询系统支持模式搜索的交互式反馈驱动调整，以适应用户感知的相似性。Peax 使用主动学习采样策略收集用户生成的二元相关性反馈。此反馈用于训练二元分类模型，以最终找到表现出与搜索目标相似的模式的其他区域。我们通过基因组学案例研究展示了 Peax 的功能，并报告了与八位领域专家一起进行的用户研究，以评估 Peax 的可用性和实用性。此外，我们在另外两个用户研究中评估了学习特征表示对视觉相似性搜索的有效性。我们发现我们的模型比其他常用技术检索出更多相似的模式。