In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenges remain. We show here, for example, that different methods, or even small variations of the same method, can produce significantly different outcomes. While intuitively somewhat surprising, this can naturally occur when applying unsupervised ML to highly dimensional data, where there can be many reasonable yet different answers to the same question. In such a case the outcome of any single unsupervised ML method should be considered a sample from a conceivably wide range of possibilities. We therefore suggest an approach that eschews finding an optimal outcome, instead facilitating the production and examination of many valid ones. This can be achieved by incorporating unsupervised ML into data visualization portals. We present here such a portal that we are developing, applied to the sample of SDSS spectra of galaxies. The main feature of the portal is interactive 2D maps of the data. Different maps are constructed by applying dimensionality reduction to different subspaces of the data, so that each map contains different information that in turn gives a different perspective on the data. The interactive maps are intuitive to use, and we demonstrate how peculiar objects and trends can be detected by means of a few button clicks. We believe that including tools in this spirit in next generation astronomical surveys will be important for making unexpected discoveries, either by professional astronomers or by citizen scientists, and will generally enable the benefits of visual inspection even when dealing with very complex and extensive datasets. Our portal is available online at galaxyportal.space.

近年来，许多工作表明，无监督机器学习（ML）可以帮助检测异常对象并揭示大型天文数据集中的趋势，但是仍然存在一些挑战。例如，我们在这里表明，不同的方法甚至同一方法的微小变化都可以产生明显不同的结果。尽管从直观上来说有些令人惊讶，但是当将无监督的ML应用于高维数据时，自然会发生这种情况，在这种情况下，对于同一问题可以有许多合理而不同的答案。在这种情况下，任何单个无监督ML方法的结果都应被视为来自各种可能范围的样本。因此，我们建议一种避免寻找最佳结果的方法，而应简化许多有效结果的产生和检查。这可以通过将无监督的ML合并到数据可视化门户中来实现。我们在这里介绍了我们正在开发的，应用于星系SDSS光谱样本的门户。门户网站的主要功能是数据的交互式2D地图。通过将降维应用于数据的不同子空间来构造不同的地图，以便每个地图都包含不同的信息，这些信息又给数据提供了不同的视角。交互式地图使用起来很直观，并且我们演示了如何通过单击几下按钮就能检测到特殊的对象和趋势。我们相信，将这种精神的工具包含在下一代天文调查中，对于通过专业天文学家或公民科学家进行意外发现至关重要，并且即使在处理非常复杂和广泛的数据集时，通常也会带来视觉检查的好处。我们的门户网站可在线访问galaxyportal.space。