Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
This work addresses the challenge of visual exploration in fields like astronomy and biology by integrating spatial texture into dimensionality reduction, though it is incremental as it builds on existing methods like t-SNE.
The paper tackled the problem of dimensionality reduction for high-dimensional images lacking spatial texture information, resulting in a method that modifies distance measures to incorporate spatial neighborhoods, with qualitative and quantitative evaluations on synthetic and real-world data.
High-dimensional imaging is becoming increasingly relevant in many fields from astronomy and cultural heritage to systems biology. Visual exploration of such high-dimensional data is commonly facilitated by dimensionality reduction. However, common dimensionality reduction methods do not include spatial information present in images, such as local texture features, into the construction of low-dimensional embeddings. Consequently, exploration of such data is typically split into a step focusing on the attribute space followed by a step focusing on spatial information, or vice versa. In this paper, we present a method for incorporating spatial neighborhood information into distance-based dimensionality reduction methods, such as t-Distributed Stochastic Neighbor Embedding (t-SNE). We achieve this by modifying the distance measure between high-dimensional attribute vectors associated with each pixel such that it takes the pixel's spatial neighborhood into account. Based on a classification of different methods for comparing image patches, we explore a number of different approaches. We compare these approaches from a theoretical and experimental point of view. Finally, we illustrate the value of the proposed methods by qualitative and quantitative evaluation on synthetic data and two real-world use cases.