Some theoretical results on discrete contour trees
- URL: http://arxiv.org/abs/2206.12123v1
- Date: Fri, 24 Jun 2022 07:31:11 GMT
- Title: Some theoretical results on discrete contour trees
- Authors: Yuqing Song
- Abstract summary: We define a discrete contour tree, called the iso-tree, on a scalar graph.
We show that the iso-tree model works for data of all dimensions, and develop an axiomatic system formalizing the discrete contour structures.
- Score: 3.1963310035729657
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Contour trees have been developed to visualize or encode scalar data in
imaging technologies and scientific simulations. Contours are defined on a
continuous scalar field. For discrete data, a continuous function is first
interpolated, where contours are then defined. In this paper we define a
discrete contour tree, called the iso-tree, on a scalar graph, and discuss its
properties. We show that the iso-tree model works for data of all dimensions,
and develop an axiomatic system formalizing the discrete contour structures. We
also report an isomorphism between iso-trees and augmented contour trees,
showing that contour tree algorithms can be used to compute discrete contour
trees, and vice versa.
Related papers
- Information-Theoretic Thresholds for Planted Dense Cycles [52.076657911275525]
We study a random graph model for small-world networks which are ubiquitous in social and biological sciences.
For both detection and recovery of the planted dense cycle, we characterize the information-theoretic thresholds in terms of $n$, $tau$, and an edge-wise signal-to-noise ratio $lambda$.
arXiv Detail & Related papers (2024-02-01T03:39:01Z) - Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure.
We introduce a new class of manifold, named soft manifold, that can solve this situation.
Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
arXiv Detail & Related papers (2023-11-29T12:48:33Z) - DeepTree: Modeling Trees with Situated Latents [8.372189962601073]
We propose a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them.
We call our deep neural model situated latent because its behavior is determined by the intrinsic state.
Our method enables generating a wide variety of tree shapes without the need to define intricate parameters.
arXiv Detail & Related papers (2023-05-09T03:33:14Z) - Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree
Canopies [5.368313160283353]
A tree skeleton compactly describes the topological structure and contains useful information.
Our method uses an instance segmentation network to detect visible trunk, branches, and twigs.
We show that our method outperforms baseline methods in highly occluded scenes.
arXiv Detail & Related papers (2023-01-20T01:46:07Z) - Individualized and Global Feature Attributions for Gradient Boosted
Trees in the Presence of $\ell_2$ Regularization [0.0]
We propose Prediction Decomposition (PreDecomp), a novel individualized feature attribution for boosted trees when they are trained with $ell$ regularization.
We also propose TreeInner, a family of debiased global feature attributions defined in terms of the inner product between any individualized feature attribution and labels on out-sample data for each tree.
arXiv Detail & Related papers (2022-11-08T17:56:22Z) - Visualizing hierarchies in scRNA-seq data using a density tree-biased
autoencoder [50.591267188664666]
We propose an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data.
We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space.
arXiv Detail & Related papers (2021-02-11T08:48:48Z) - Robust estimation of tree structured models [0.0]
We show that it is possible to recover trees from noisy binary data up to a small equivalence class of possible trees.
We also provide a characterisation of when the Chow-Liu algorithm consistently learns the underlying tree from the noisy data.
arXiv Detail & Related papers (2021-02-10T14:58:40Z) - SGA: A Robust Algorithm for Partial Recovery of Tree-Structured
Graphical Models with Noisy Samples [75.32013242448151]
We consider learning Ising tree models when the observations from the nodes are corrupted by independent but non-identically distributed noise.
Katiyar et al. (2020) showed that although the exact tree structure cannot be recovered, one can recover a partial tree structure.
We propose Symmetrized Geometric Averaging (SGA), a more statistically robust algorithm for partial tree recovery.
arXiv Detail & Related papers (2021-01-22T01:57:35Z) - Linguistically Driven Graph Capsule Network for Visual Question
Reasoning [153.76012414126643]
We propose a hierarchical compositional reasoning model called the "Linguistically driven Graph Capsule Network"
The compositional process is guided by the linguistic parse tree. Specifically, we bind each capsule in the lowest layer to bridge the linguistic embedding of a single word in the original question with visual evidence.
Experiments on the CLEVR dataset, CLEVR compositional generation test, and FigureQA dataset demonstrate the effectiveness and composition generalization ability of our end-to-end model.
arXiv Detail & Related papers (2020-03-23T03:34:25Z) - PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree
Conditions [66.87405921626004]
This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation.
We propose a conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles the structural and geometric factors.
arXiv Detail & Related papers (2020-03-19T08:27:25Z)
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.