Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms.

中文翻译：

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投影作为分类系统设计的视觉辅助工具

降维是高维数据可视化的一个引人注目的替代方案。该方法通过将观测值（高维向量）之间的关系映射到低（二维或三维）空间来洞察高维特征空间。这些低维表示支持基于直接可视化的异常值和组检测等任务。监督学习是机器学习的一个子领域，也与观察有关。监督学习中的一个关键任务包括根据以往经验的概括为观察分配类标签。此类分类系统的有效开发取决于多种选择，包括特征描述符、学习算法和超参数。这些选择并非微不足道，并且没有简单的方法可以改进性能不佳的分类系统。在这种情况下，我们首先建议使用基于降维（投影）的视觉表示来预测分类效果的反馈。其次，我们提出了一种基于投影的可视化分析方法和支持工具，可用于通过特征选择来改进分类系统。我们通过涉及四个数据集和三个代表性学习算法的实验来评估我们的提议。可用于通过特征选择改进分类系统。我们通过涉及四个数据集和三个代表性学习算法的实验来评估我们的提议。可用于通过特征选择改进分类系统。我们通过涉及四个数据集和三个代表性学习算法的实验来评估我们的提议。