Invention Grant
- Patent Title: Generating a high-dimensional network graph for data visualization utilizing landmark data points and modularity-based manifold tearing
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Application No.: US16850677Application Date: 2020-04-16
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Publication No.: US11295491B2Publication Date: 2022-04-05
- Inventor: Nupur Kumari , Piyush Gupta , Akash Rupela , Siddarth R , Balaji Krishnamurthy
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Jolley Preece
- Main IPC: G06T11/20
- IPC: G06T11/20 ; G06F17/11

Abstract:
The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.
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Information query