Prototypical Visualization: Using Prototypical Networks for Visualizing Large Unstructured Data | TU Wien

Information

Publication Type: Master Thesis
Workgroup(s)/Project(s):
- Joint Human-Machine Data Exploration
Date: 2025
TU Wien Library: AC17416388
Open Access: yes
First Supervisor: Manuela Waldner
Pages: 62
Keywords: Dimensionality Reduction, Data Visualization, Prototypical Network, ProtoNet, Class Separation, Scatter Plot

Abstract

Making sense of data is something that many professionals are required to do on a daily basis. This can be a difficult task if the amount of data is so large that it can not be easily examined. One effective method of quickly getting an overview of data structure is visualization, but this is not always a feasible solution with large data due to the sheer amount of data and also the potentially high dimensionality. Machine learning models can help with with the organization and classification of data, but they often require large quantities of labeled training data, which is frequently not readily available. This is why models that can reliably classify data based on only few examples for each class are an interesting topic of research. One such kind of model are prototypical networks. They utilize few samples to create an embedding space in fewer dimensions, in which similar data points cluster around a single class prototype. In this thesis, we investigate if the embedding space of a prototypical network makes for a good approach for the purpose of visualizing high-dimensional, unstructured data. The goal is to reduce the dimensionality of the data in such a way that the highdimensional relations and structures between data points are preserved, resulting in 2D representations of the data that form coherent class clusters in a scatter plot visualization. This approach is compared with, and evaluated against, other well known supervised and unsupervised dimensionality reduction techniques. Through quantitative experiments relying on statistical measures, as well as a qualitative evaluation of our results, we find that our ProtoNet is capable of producing point embeddings in which the spatial separation of classes is as good or better than the other methods.

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thesis

Weblinks

BibTeX

@mastersthesis{stoff-2025-pvu,
  title =      "Prototypical Visualization: Using Prototypical Networks for
               Visualizing Large Unstructured Data",
  author =     "Mario Stoff",
  year =       "2025",
  abstract =   "Making sense of data is something that many professionals
               are required to do on a daily basis. This can be a difficult
               task if the amount of data is so large that it can not be
               easily examined. One effective method of quickly getting an
               overview of data structure is visualization, but this is not
               always a feasible solution with large data due to the sheer
               amount of data and also the potentially high dimensionality.
               Machine learning models can help with with the organization
               and classification of data, but they often require large
               quantities of labeled training data, which is frequently not
               readily available. This is why models that can reliably
               classify data based on only few examples for each class are
               an interesting topic of research. One such kind of model are
               prototypical networks. They utilize few samples to create an
               embedding space in fewer dimensions, in which similar data
               points cluster around a single class prototype. In this
               thesis, we investigate if the embedding space of a
               prototypical network makes for a good approach for the
               purpose of visualizing high-dimensional, unstructured data.
               The goal is to reduce the dimensionality of the data in such
               a way that the highdimensional relations and structures
               between data points are preserved, resulting in 2D
               representations of the data that form coherent class
               clusters in a scatter plot visualization. This approach is
               compared with, and evaluated against, other well known
               supervised and unsupervised dimensionality reduction
               techniques. Through quantitative experiments relying on
               statistical measures, as well as a qualitative evaluation of
               our results, we find that our ProtoNet is capable of
               producing point embeddings in which the spatial separation
               of classes is as good or better than the other methods.",
  pages =      "62",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "Dimensionality Reduction, Data Visualization, Prototypical
               Network, ProtoNet, Class Separation, Scatter Plot",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/stoff-2025-pvu/",
}