The growing prevalence of high-dimensional data has fostered the development of multidimensional projection (MP) techniques, such as t-SNE, UMAP, and LAMP, for data visualization and exploration. However, conventional MP methods typically employ generic quality metrics, neglecting individual user preferences. This study proposes a new framework that tailors MP techniques based on user-specific quality criteria, enhancing projection interpretability. Our approach combines three visual quality metrics, stress, neighborhood preservation, and silhouette score, to create a composite metric for a precise MP evaluation. We then optimize the projection scale by maximizing the composite metric value. We conducted an experiment involving two users with different projection preferences, generating projections using t-SNE, UMAP, and LAMP. Users rate projections according to their criteria, producing two training sets. We derive optimal weights for each set and apply them to other datasets to determine the best projections per user. Our findings demonstrate that personalized projections effectively capture user preferences, fostering better data exploration and enabling more informed decision-making. This user-centric approach promotes advancements in multidimensional projection techniques that accommodate diverse user preferences and enhance interpretability.

翻译：随着高维数据的日益普及，多维投影（MP）技术（如t-SNE、UMAP和LAMP）在数据可视化与探索领域得到了广泛发展。然而，传统MP方法通常采用通用质量度量，忽略了用户的个体偏好。本研究提出了一种新框架，该框架基于用户特定的质量标准定制MP技术，从而提升投影的可解释性。我们的方法结合了三种视觉质量度量——应力、邻域保持度和轮廓系数，构建了一个用于精确评估MP的复合度量。随后，我们通过最大化该复合度量值来优化投影的缩放比例。我们设计了一项实验，邀请两名具有不同投影偏好的用户参与，使用t-SNE、UMAP和LAMP生成投影。用户根据各自的标准对投影进行评分，形成两个训练集。我们为每个训练集推导出最优权重，并将其应用于其他数据集，以确定每位用户的最佳投影。研究结果表明，个性化投影能有效捕捉用户偏好，促进更高效的数据探索并支持更明智的决策。这种以用户为中心的方法推动了多维投影技术的进步，使其能够适应多样化的用户需求并增强可解释性。