Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM

• URL: http://arxiv.org/abs/2201.00701v1
• Date: Mon, 3 Jan 2022 15:06:22 GMT
• Title: Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM
• Authors: Adam \v{S}melko, So\v{n}a Moln\'arov\'a, Miroslav Kratochv\'il, Abhishek Koladiya, Jan Musil, Martin Kruli\v{s}, Ji\v{r}\'i Vondr\'a\v{s}ek
• Abstract summary: BlosSOM is a high-performance semi-supervised dimensionality reduction software for interactive user-steerable visualization of high-dimensional datasets. We show the application of BlosSOM on realistic datasets, where it helps to produce high-quality visualizations that incorporate user-specified layout and focus on certain features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many different methods exist, their performance is often insufficient for providing quick insight into many contemporary datasets, and the unsupervised mode of use prevents the users from utilizing the methods for dataset exploration and fine-tuning the details for improved visualization quality. We present BlosSOM, a high-performance semi-supervised dimensionality reduction software for interactive user-steerable visualization of high-dimensional datasets with millions of individual data points. BlosSOM builds on a GPU-accelerated implementation of the EmbedSOM algorithm, complemented by several landmark-based algorithms for interfacing the unsupervised model learning algorithms with the user supervision. We show the application of BlosSOM on realistic datasets, where it helps to produce high-quality visualizations that incorporate user-specified layout and focus on certain features. We believe the semi-supervised dimensionality reduction will improve the data visualization possibilities for science areas such as single-cell cytometry, and provide a fast and efficient base methodology for new directions in dataset exploration and annotation.

Related papers

• VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation [0.0]
  Visual Explanations via Region (VERA) is an automatic embedding-annotation approach that generates visual explanations for any two-dimensional embedding. VERA produces informative explanations that characterize distinct regions in the embedding space, allowing users to gain an overview of the embedding landscape at a glance. We illustrate the usage of VERA on a real-world data set and validate the utility of our approach with a comparative user study.
  arXiv  Detail & Related papers  (2024-06-07T10:23:03Z)
• Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques [1.2328446298523066]
  Hyperspectral imaging provides precise classification for land use and cover due to its exceptional spectral resolution. However, the challenges of high dimensionality and limited spatial resolution hinder its effectiveness. This study addresses these challenges by employing deep learning techniques to efficiently process, extract features, and classify data in an integrated manner.
  arXiv  Detail & Related papers  (2024-03-03T15:47:43Z)
• MDFL: Multi-domain Diffusion-driven Feature Learning [19.298491870280213]
  We present a multi-domain diffusion-driven feature learning network (MDFL) MDFL redefines the effective information domain that the model really focuses on. We demonstrate that MDFL significantly improves the feature extraction performance of high-dimensional data.
  arXiv  Detail & Related papers  (2023-11-16T02:55:21Z)
• SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models [0.0]
  Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts. In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, diverse, and correspondingly accurate satellite images is implemented. The effectiveness of our proposed model is validated using a meticulously labeled dataset introduced within the context of this study.
  arXiv  Detail & Related papers  (2023-09-28T19:39:13Z)
• ShaRP: Shape-Regularized Multidimensional Projections [71.30697308446064]
  We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot. ShaRP scales well with dimensionality and dataset size, and generically handles any quantitative dataset.
  arXiv  Detail & Related papers  (2023-06-01T11:16:58Z)
• Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization [20.43471678277403]
  We propose LaptSNE, a new graph-based dimensionality reduction method based on t-SNE. Specifically, LaptSNE leverages the eigenvalue information of the graph Laplacian to shrink the potential clusters in the low-dimensional embedding. We show how to calculate the gradient analytically, which may be of broad interest when considering optimization with Laplacian-composited objective.
  arXiv  Detail & Related papers  (2022-07-25T14:10:24Z)
• FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks [77.34726150561087]
  We show how to explore high-dimensional landscape characteristics of neural networks. We generalize observations on small neural networks to more complex systems. An interactive dashboard opens up a number of possible application networks.
  arXiv  Detail & Related papers  (2022-04-09T16:41:53Z)
• Self-supervised Human Detection and Segmentation via Multi-view Consensus [116.92405645348185]
  We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training. We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
  arXiv  Detail & Related papers  (2020-12-09T15:47:21Z)
• NCVis: Noise Contrastive Approach for Scalable Visualization [79.44177623781043]
  NCVis is a high-performance dimensionality reduction method built on a sound statistical basis of noise contrastive estimation. We show that NCVis outperforms state-of-the-art techniques in terms of speed while preserving the representation quality of other methods.
  arXiv  Detail & Related papers  (2020-01-30T15:43:50Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)
• GridMask Data Augmentation [76.79300104795966]
  We propose a novel data augmentation method GridMask' in this paper. It utilizes information removal to achieve state-of-the-art results in a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-01-13T07:27:05Z)

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.