Parametric nonlinear dimensionality reduction using kernel t-SNE

Gisbrecht A, Schulz A, Hammer B. Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing. 2015;147:71-82

Parametric t-Distributed Stochastic Exemplar-centered Embedding

Parametric embedding methods such as parametric t-SNE (pt-SNE) have been widely adopted for data visualization and out-of-sample data embedding without further computationally expensive optimization or approximation. However, the performance of pt-SNE is highly sensitive to the hyper-parameter batch size due to conflicting optimization goals, and often produces dramatically different embeddings with different choices of user-defined perplexities. To effectively solve these issues, we present parametric t-distributed stochastic exemplar-centered embedding methods. Our strategy learns embedding parameters by comparing given data only with precomputed exemplars, resulting in a cost function with linear computational and memory complexity, which is further reduced by noise contrastive samples. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed methods significantly outperform pt-SNE in terms of robustness, visual effects, and quantitative evaluations.Comment: fixed typo

Continuous Interaction With a Smart Speaker via Low-Dimensional Embeddings of Dynamic Hand Pose

This paper presents a new continuous interaction strategy with visual feedback of hand pose and mid-air gesture recognition and control for a smart music speaker, which utilizes only 2 video frames to recognize gestures. Frame-based hand pose features from MediaPipe Hands, containing 21 landmarks, are embedded into a 2 dimensional pose space by an autoencoder. The corresponding space for interaction with the music content is created by embedding high-dimensional music track profiles to a compatible two-dimensional embedding. A PointNet-based model is then applied to classify gestures which are used to control the device interaction or explore music spaces. By jointly optimising the autoencoder with the classifier, we manage to learn a more useful embedding space for discriminating gestures. We demonstrate the functionality of the system with experienced users selecting different musical moods by varying their hand pose

Parametric UMAP embeddings for representation and semi-supervised learning

UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data. The UMAP algorithm consists of two steps: (1) Compute a graphical representation of a dataset (fuzzy simplicial complex), and (2) Through stochastic gradient descent, optimize a low-dimensional embedding of the graph. Here, we extend the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding. We first demonstrate that Parametric UMAP performs comparably to its non-parametric counterpart while conferring the benefit of a learned parametric mapping (e.g. fast online embeddings for new data). We then explore UMAP as a regularization, constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data. Google Colab walkthrough: https://colab.research.google.com/drive/1WkXVZ5pnMrm17m0YgmtoNjM_XHdnE5Vp?usp=sharin

Integrated fecal microbiome–metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS) several modalities of biological and clinical data must be combined. We aimed to identify profiles of faecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Faecal metabolites were measured using proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy and gut microbiome using Shotgun Metagenomic Sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HC) with extensive clinical, biological and phenotype information. Data were analysed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve (AUC) of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on faecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavourable stress-response related to gastrointestinal symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS

ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths

In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but analysis has focused almost exclusively on the self-similarity of single trajectories. In contrast, we describe patterns emerging from drawing many trajectories---for different initial conditions, end states, and solution strategies---in the same embedding space. We argue that general statements about the problem-solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik's cube), strategy games (chess), and optimization problems (neural network training). We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding.Comment: Final version; accepted for publication in the ACM TiiS Special Issue on "Interactive Visual Analytics for Making Explainable and Accountable Decisions