TITLE: Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets 

ABSTRACT: We introduce the Collection Space Navigator (CSN), a browser-based
visualization tool to explore, research, and curate large collections of visual
digital artifacts that are associated with multidimensional data, such as
vector embeddings or tables of metadata. Media objects such as images are often
encoded as numerical vectors, for e.g. based on metadata or using machine
learning to embed image information. Yet, while such procedures are widespread
for a range of applications, it remains a challenge to explore, analyze, and
understand the resulting multidimensional spaces in a more comprehensive
manner. Dimensionality reduction techniques such as t-SNE or UMAP often serve
to project high-dimensional data into low dimensional visualizations, yet
require interpretation themselves as the remaining dimensions are typically
abstract. Here, the Collection Space Navigator provides a customizable
interface that combines two-dimensional projections with a set of configurable
multidimensional filters. As a result, the user is able to view and investigate
collections, by zooming and scaling, by transforming between projections, by
filtering dimensions via range sliders, and advanced text filters. Insights
that are gained during the interaction can be fed back into the original data
via ad hoc exports of filtered metadata and projections. This paper comes with
a functional showcase demo using a large digitized collection of classical
Western art. The Collection Space Navigator is open source. Users can
reconfigure the interface to fit their own data and research needs, including
projections and filter controls. The CSN is ready to serve a broad community.
We introduce the Collection Space Navigator (CSN), a browser-based
visualization tool to explore, research, and curate large collections of visual
digital artifacts that are associated with multidimensional data, such as
vector embeddings or tables of metadata. Media objects such as images are often
encoded as numerical vectors, for e.g. based on metadata or using machine
learning to embed image information. Yet, while such procedures are widespread
for a range of applications, it remains a challenge to explore, analyze, and
understand the resulting multidimensional spaces in a more comprehensive
manner. Dimensionality reduction techniques such as t-SNE or UMAP often serve
to project high-dimensional data into low dimensional visualizations, yet
require interpretation themselves as the remaining dimensions are typically
abstract. Here, the Collection Space Navigator provides a customizable
interface that combines two-dimensional projections with a set of configurable
multidimensional filters. As a result, the user is able to view and investigate
collections, by zooming and scaling, by transforming between projections, by
filtering dimensions via range sliders, and advanced text filters. Insights
that are gained during the interaction can be fed back into the original data
via ad hoc exports of filtered metadata and projections. This paper comes with
a functional showcase demo using a large digitized collection of classical
Western art. The Collection Space Navigator is open source. Users can
reconfigure the interface to fit their own data and research needs, including
projections and filter controls. The CSN is ready to serve a broad community.
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
http://arxiv.org/abs/2305.06809v1