Scientists aim to extract simplicity from observations of the complex world.
An important component of this process is the exploration of data in search of
trends. In practice, however, this tends to be more of an art than a science.
Among all trends existing in the natural world, one-dimensional trends, often
called sequences, are of particular interest as they provide insights into
simple phenomena. However, some are challenging to detect as they may be
expressed in complex manners. We present the Sequencer, an algorithm designed
to generically identify the main trend in a dataset. It does so by constructing
graphs describing the similarities between pairs of observations, computed with
a set of metrics and scales. Using the fact that continuous trends lead to more
elongated graphs, the algorithm can identify which aspects of the data are
relevant in establishing a global sequence. Such an approach can be used beyond
the proposed algorithm and can optimize the parameters of any dimensionality
reduction technique. We demonstrate the power of the Sequencer using real-world
data from astronomy, geology as well as images from the natural world. We show
that, in a number of cases, it outperforms the popular t-SNE and UMAP
dimensionality reduction techniques. This approach to exploratory data
analysis, which does not rely on training nor tuning of any parameter, has the
potential to enable discoveries in a wide range of scientific domains. The
source code is available on github and we provide an online interface at
http://sequencer.org.

提出了一种名为 “Sequencer” 的算法，其通过构建描述观测对之间相似性的图表，使用一组度量标准和比例计算相似性，以确定数据中的主要趋势，进而用于优化任何降维技术的参数。该算法不依赖于训练或任何参数的调整，可用于探索性数据分析，具有潜在的在各种科学领域中发现信息的优点。