LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Data

2026-02-11

AI Summary

Existing inverse projection methods are constrained by fixed manifold structures, limiting their ability to adequately capture the diversity of high-dimensional image data and hindering their applicability in tasks such as data augmentation, classifier analysis, and data imputation. This work proposes a general and controllable inverse projection framework that enables flexible exploration and reconstruction of the high-dimensional space underlying any dimensionality reduction technique—such as t-SNE or UMAP—through two intuitive, user-defined parameters. By transcending conventional structural limitations, the method offers broad compatibility, ease of implementation, and strong user-guided control. Its effectiveness is demonstrated in applications like image style transfer, where it achieves more comprehensive and practical coverage of the high-dimensional data space.

Technology Category

• Machine Learning: Learning with Manifolds
• Computer Vision: Representation Learning for Vision
• Data Mining & Knowledge Management: Data Compression

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Projections (or dimensionality reduction) methods $P$ aim to map high-dimensional data to typically 2D scatterplots for visual exploration. Inverse projection methods $P^{-1}$ aim to map this 2D space to the data space to support tasks such as data augmentation, classifier analysis, and data imputation. Current $P^{-1}$ methods suffer from a fundamental limitation -- they can only generate a fixed surface-like structure in data space, which poorly covers the richness of this space. We address this by a new method that can `sweep'the data space under user control. Our method works generically for any $P$ technique and dataset, is controlled by two intuitive user-set parameters, and is simple to implement. We demonstrate it by an extensive application involving image manipulation for style transfer.

Problem

• inverse projection
• high-dimensional data
• dimensionality reduction
• data space coverage
• visualization

Innovation

• inverse projection
• dimensionality reduction
• loss-controlled
• data space exploration
• image manipulation