Discovering the Latent Similarities of the KNN Graph by Metric Transformation
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Summary
This paper investigates metric transformation which aims at finding new functional relationship to dig the latent similarity by incorporating the new penalized consensus information (PCI), and shows that PCI works superior compared with the original consensus information for denoising.
- Type
- article
- Published
- 2015-06-22
- Cited by
- 1
- References
- 17
- OpenAlex
- https://openalex.org/W2010888002
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17740881
Keywords
Computer science, Robustness (evolution), Graph, Similarity (geometry), Metric (unit)
References
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- Shape google: Geometric words and expressions for invariant shape retrieval
- Consensus of k-NNs for Robust Neighborhood Selection on Graph-Based Manifolds
- Affinity learning via self-diffusion for image segmentation and clustering
- Unsupervised metric learning by Self-Smoothing Operator
- The Princeton Shape Benchmark
- Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
- Clustering and Embedding Using Commute Times
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
- Diffusion Processes for Retrieval Revisited
- Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval
- Diffusion maps
- Et al
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