Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

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. Consequently, exploration of such data is Figure 1: Texture-aware dimensionality reduction. An image typically split into a step focusing on the attribute space followed by (a) with black and white pixels forms multiple textures. In this paper, distance-based dimensionality reduction produces one cluster of we present a method for incorporating spatial neighborhood information black and one cluster of white pixels (b), a texture-aware version into distance-based dimensionality reduction methods, such as should create clusters for the different textures (c). 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 The spatial configuration is, however, commonly of interest when of different methods for comparing image patches, we explore a analyzing high-dimensional image data. We compare these approaches from neighborhood information into account, in addition to highdimensional a theoretical and experimental point of view. Typical approaches to combine high-dimensional evaluation on synthetic data and two real-world use cases. They use the embedding as a colormap and perform segmentation on the re-colored image. High-dimensional data is commonly acquired and analyzed in various Decoupling the high-dimensional and spatial analysis in such a application domains, from systems biology [26] to insurance way has several downsides: Most importantly, boundaries between fraud detection [37]. Typically, high-dimensional data are tabular clusters in an embedding are often not well defined, and as such data with many columns (or attributes), corresponding to the dimensionality classification is ambiguous and has a level of arbitrariness.