Beyond Quantification of Neighborhoods: Perception-Driven Quality Measures for Dimensionality Reduction
Authors
Jiazhi Xia (Central South University), Chunxi Deng (Central South University), Xiangyu Zhu (Central South University)
Presentation
- Session
- Let's dig into the data (from China)
- Time
- Friday, Nov 13, 08:24 – 08:36 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall America center
Keywords
High-dimensional Data ; Methodologies ; Perception & Cognition ; Dimensionality Reduction
Abstract
Information distortion is inevitable in the dimensionality reduction of high-dimensional data. To help understand and evaluate such distortions, researchers have proposed various quality measures for projections. Among them, measures based on quantifying neighborhood preservation are widely adopted. However, these measures do not always align with human visual perception. For instance, a projection may achieve a neighborhood preservation score above 0.99, while its k-nearest neighbors in the high-dimensional space are mapped beyond the 5k-neighborhood in the projection space. Such misalignment between perceived projection quality and quantitative measures can severely mislead users' interpretation of the resulting projections. To bridge this gap, we revisit projection quality assessment through the lens of visual perception. Our work unfolds in four key steps: First, we introduce a neighborhood-based labeling scheme for projection quality, establishing a reliable foundation for perception-driven evaluation. Second, we design a carefully curated sample set paired with a pairwise contrast-based labeling strategy, which significantly reduces annotation effort while maintaining effectiveness. Third, leveraging these labeled samples, we train a deep neural network to learn and simulate human visual perception of neighborhood preservation, resulting in a novel perception-driven quality measure. Finally, armed with this perception-driven measure, we conduct a systematic comparative evaluation of existing neighborhood quantification-based measures, providing perception-grounded insights into them.
For Practitioners
This paper will be of particular interest to researchers and practitioners in data visualization and high-dimensional data projection. They can use the proposed neighborhood views and perception-driven evaluation framework to inspect local neighborhood distortions, assess projection quality, and make more informed choices among dimensionality reduction methods and quality metrics.