标题： 对比维度降低：一项系统综述 显示英文标题

标题： Contrastive Dimension Reduction: A Systematic Review

摘要： 对比维度约简（CDR）方法旨在提取相对于对照（背景）组，处理（前景）组特有的或富集的信号。 这种情形出现在许多科学领域，如基因组学、成像和时间序列分析中，传统的维度约简技术如主成分分析（PCA）可能无法隔离感兴趣的信号。 在本综述中，我们提供了现有CDR方法的系统概述。 我们提出了一种用于分析病例对照研究的流程，并基于其假设、目标和数学公式对CDR方法进行分类，将不同的方法统一在一个共享的概念框架下。 我们强调了现有CDR方法中的关键应用和挑战，并识别了开放性问题和未来方向。 通过提供一个清晰的CDR及其应用框架，我们旨在促进该新兴领域的更广泛应用并激发进一步的发展。 显示英文摘要

摘要： Contrastive dimension reduction (CDR) methods aim to extract signal unique to or enriched in a treatment (foreground) group relative to a control (background) group. This setting arises in many scientific domains, such as genomics, imaging, and time series analysis, where traditional dimension reduction techniques such as Principal Component Analysis (PCA) may fail to isolate the signal of interest. In this review, we provide a systematic overview of existing CDR methods. We propose a pipeline for analyzing case-control studies together with a taxonomy of CDR methods based on their assumptions, objectives, and mathematical formulations, unifying disparate approaches under a shared conceptual framework. We highlight key applications and challenges in existing CDR methods, and identify open questions and future directions. By providing a clear framework for CDR and its applications, we aim to facilitate broader adoption and motivate further developments in this emerging field.