Uncovering Temporal Patterns in Visualizations of High-Dimensional Data

• URL: http://arxiv.org/abs/2403.19040v2
• Date: Sat, 14 Dec 2024 13:35:23 GMT
• Title: Uncovering Temporal Patterns in Visualizations of High-Dimensional Data
• Authors: Pavlin G. Poličar, Blaž Zupan,
• Abstract summary: A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration.<n>We propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration. However, popular embedding techniques, such as t-SNE and UMAP, typically assume that data points are independent. When this assumption is violated, as in time-series data, the resulting visualizations may fail to reveal important temporal patterns and trends. To address this, we propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations. Through a series of experiments on both synthetic and real-world datasets, we demonstrate that our approach effectively uncovers temporal patterns and improves the interpretability of the visualizations. Furthermore, the method improves temporal coherence while preserving the fidelity of the embeddings, providing a robust tool for dynamic data analysis.

Related papers

• Seurat: From Moving Points to Depth [66.65189052568209]
  We propose a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories. Our approach achieves temporally smooth, high-accuracy depth predictions across diverse domains.
  arXiv  Detail & Related papers  (2025-04-20T17:37:02Z)
• An Incremental Non-Linear Manifold Approximation Method [0.0]
  This research develops an incremental non-linear dimension reduction method using the Geometric Multi-Resolution Analysis (GMRA) framework for streaming data. The proposed method enables real-time data analysis and visualization by incrementally updating the cluster map, basis PCA vectors, and wavelet coefficients.
  arXiv  Detail & Related papers  (2025-04-12T03:54:05Z)
• Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA [8.474631244771928]
  stPCA represents dynamics of a high-dimensional time-series by only a single latent variable without distortion. The dynamics of this single variable is analytically solved and theoretically preserves the temporal property of original time-series. Its applications to real-world datasets demonstrated the effectiveness of stPCA.
  arXiv  Detail & Related papers  (2025-01-22T02:09:52Z)
• Deep End-to-End Survival Analysis with Temporal Consistency [49.77103348208835]
  We present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. A central idea in our method is temporal consistency, a hypothesis that past and future outcomes in the data evolve smoothly over time. Our framework uniquely incorporates temporal consistency into large datasets by providing a stable training signal.
  arXiv  Detail & Related papers  (2024-10-09T11:37:09Z)
• Gaussian Embedding of Temporal Networks [12.09704836128003]
  TGNE (textbfTemporal textbfGaussian textbfNetwork textbfEmbedding) bridges statistical analysis of networks via Latent Space Models (LSM)citeHoff2002 and temporal graph machine learning. We evaluate TGNE's effectiveness in reconstructing the original graph and modelling uncertainty.
  arXiv  Detail & Related papers  (2024-05-27T15:07:57Z)
• Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths [54.53208538517505]
  This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW) We propose a DTW layer based around bi-level optimisation and deep declarative networks, which we name DecDTW. We show that this property is particularly useful for applications where downstream loss functions are defined on the optimal alignment path itself.
  arXiv  Detail & Related papers  (2023-03-19T21:58:37Z)
• SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series [23.074319429090092]
  We propose SOM-CPC, a model that visualizes data in an organized 2D manifold, while preserving higher-dimensional information. We show on both synthetic and real-life data (physiological data and audio recordings) that SOM-CPC outperforms strong baselines like DL-based feature extraction.
  arXiv  Detail & Related papers  (2022-05-31T15:21:21Z)
• A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data [0.3749861135832073]
  Recently proposed extensions to VAEs that can handle temporal and longitudinal data have applications in healthcare, behavioural modelling, and predictive maintenance. We propose the heterogeneous longitudinal VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to heterogeneous data. HL-VAE provides efficient inference for high-dimensional datasets and includes likelihood models for continuous, count, categorical, and ordinal data.
  arXiv  Detail & Related papers  (2022-04-20T10:18:39Z)
• IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment [58.8330387551499]
  We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) We propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2022-03-22T10:14:08Z)
• Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments [110.79706180350507]
  We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality. Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
  arXiv  Detail & Related papers  (2022-03-11T09:46:22Z)
• Topology-Preserving Dimensionality Reduction via Interleaving Optimization [10.097180927318703]
  We show how optimization seeking to minimize the interleaving distance can be incorporated into dimensionality reduction algorithms. We demonstrate the utility of this framework to data visualization.
  arXiv  Detail & Related papers  (2022-01-31T06:11:17Z)
• Self-Attention Neural Bag-of-Features [103.70855797025689]
  We build on the recently introduced 2D-Attention and reformulate the attention learning methodology. We propose a joint feature-temporal attention mechanism that learns a joint 2D attention mask highlighting relevant information.
  arXiv  Detail & Related papers  (2022-01-26T17:54:14Z)
• Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition [62.46544616232238]
  Previous motion recognition methods have achieved promising performance through the tightly coupled multi-temporal representation. We propose to decouple and recouple caused caused representation for RGB-D-based motion recognition.
  arXiv  Detail & Related papers  (2021-12-16T18:59:47Z)
• PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series [5.911865723926626]
  Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
  arXiv  Detail & Related papers  (2021-09-30T20:01:19Z)
• Joint Characterization of Spatiotemporal Data Manifolds [0.0]
  Dimensionality reduction (DR) is a type of characterization designed to mitigate the "curse of dimensionality" on high-D signals. Recent years have seen the additional development of a suite of nonlinear DR algorithms, frequently categorized as "manifold learning" Here, we show these three DR approaches can yield complementary information about ST manifold topology.
  arXiv  Detail & Related papers  (2021-08-21T16:42:22Z)
• ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer [2.4366811507669124]
  We propose a Spatio-Temporal Transformer-based architecture for object detection from a sequence of temporal frames. We employ the full attention mechanisms to take advantage of the features correlations over both dimensions. Results show a significant 5% mAP improvement on the KITTI MOD dataset.
  arXiv  Detail & Related papers  (2021-07-13T07:38:08Z)
• Interpretable Time-series Representation Learning With Multi-Level Disentanglement [56.38489708031278]
  Disentangle Time Series (DTS) is a novel disentanglement enhancement framework for sequential data. DTS generates hierarchical semantic concepts as the interpretable and disentangled representation of time-series. DTS achieves superior performance in downstream applications, with high interpretability of semantic concepts.
  arXiv  Detail & Related papers  (2021-05-17T22:02:24Z)
• OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation [51.083573770706636]
  This work uses relational inference to fill in the incomplete data. We propose Omni-Relational Network (OR-Net) to model the pointwise relativity in two aspects.
  arXiv  Detail & Related papers  (2021-05-02T06:05:54Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Time-aware Graph Embedding: A temporal smoothness and task-oriented approach [9.669206664225234]
  This paper presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally-aware information.
  arXiv  Detail & Related papers  (2020-07-22T02:20:25Z)
• Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering [50.43424130281065]
  We propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously damage the spatial information of the data by converting 2D data to vectors in a preprocessing step.
  arXiv  Detail & Related papers  (2020-05-19T05:54:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.