论文标题
通过主动搜索，交互式可视化中的指导数据发现
Guided Data Discovery in Interactive Visualizations via Active Search
论文作者
论文摘要
视觉分析的最新进展使我们能够从用户互动和发现分析目标中学习。这些创新为在数据探索过程中积极指导用户奠定了基础。随着数据集的规模和复杂性的增长，提供此类指导将变得更加关键，从而排除了详尽的调查。同时，机器学习社区还在数据集的大小和复杂性增长，排除了详尽的标签。积极学习是在培训期间为积极指导模型开发的广泛算法系列。我们将考虑这些类似的研究推力的交集。首先，我们讨论将主动学习算法选择与手头任务相匹配的细微差别。这对于性能至关重要，这是我们在模拟研究中证明的事实。然后，我们为通过专门为该任务设计的主动学习算法指导的数据发现的特定任务提供了用户研究的结果。
Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users during data exploration. Providing such guidance will become more critical as datasets grow in size and complexity, precluding exhaustive investigation. Meanwhile, the machine learning community also struggles with datasets growing in size and complexity, precluding exhaustive labeling. Active learning is a broad family of algorithms developed for actively guiding models during training. We will consider the intersection of these analogous research thrusts. First, we discuss the nuances of matching the choice of an active learning algorithm to the task at hand. This is critical for performance, a fact we demonstrate in a simulation study. We then present results of a user study for the particular task of data discovery guided by an active learning algorithm specifically designed for this task.
