Query-Augmented Active Metric Learning

Deng, Yujia; Yuan, Yirang; Fu, Haoda; Qu, Annie

doi:10.1080/01621459.2021.2019045

Cited by 6 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although COBRA quickly improves the NMI and ARI values, it cannot further enhance the clustering even when more queries are invested (all mustlink clusters have already been discovered), leading to a performance ceiling dictated by cluster purities. Recent methods like COBRAS (Van Craenendonck et al, 2018) and AQM+MEE (Deng et al, 2023b) also encounter similar issues. In contrast, A3S not only delivers high-quality clusters but also identifies a subset of outlier samples.…”

Section: The Detailed Running Results Of A3s Is Inmentioning

confidence: 99%

HCPC: A New Parsimonious Clustering Method based on Hierarchical Characters for Morphological Phylogenetic Reconstruction

Feng

Liu

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Phylogenetic trees are reconstructed frequently to provide a better interpretation of the evolutionary history of species. However, most traditional methods ignore the hierarchical relationships among characters and neglect the inapplicable state that frequently exists in the morphological data, resulting in poor performance of the phylogenetic analysis.Results: In this study, we propose a phylogenetic clustering method based on hierarchical characters. Accordingly, we call our method Hierarchical Characters Parsimonious Clustering(HCPC). To combine prior phylogenetic knowledge and treat the inapplicable state more reasonably, two stages are proposed, i.e., Phylogenetic reconstruction and parsimonious tree search. During phylogenetic reconstruction, HCPC is able to infer the shared ancestral relationships among species. For the search of the parsimonious tree, we use a simulated annealing algorithm to heuristically search the phylogenetic tree based on the parsimony criterion. In addition, HCPC combines asymmetric binary relationships and character hierarchies to solve the problem of the ambiguity of the inapplicable state.Conclusion: The experimental results show that the proposed method provides better performance of phylogenetic analysis than existing methods and a scientific and quantitative basis for biologists to study species evolution.

show abstract

Section: The Detailed Running Results Of A3s Is Inmentioning

confidence: 99%

HCPC: A New Parsimonious Clustering Method based on Hierarchical Characters for Morphological Phylogenetic Reconstruction

Feng

Liu

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Bibrecord-Based Literature Management with Interactive Latent Space Learning

Watanabe

Ito

Matsubara

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Human-in-the-loop latent space learning for biblio-record-based literature management

Watanabe,

Ito,

Matsubara

et al. 2024

Int J Digit Libr

View full text Add to dashboard Cite

Every researcher must conduct a literature review, and the document management needs of researchers working on various research topics vary. However, there are two major challenges. First, traditional methods such as the tree hierarchy of document folders and tag-based management are no longer effective with the enormous volume of publications. Second, although their bibliographic information is available to everyone, many papers can only be accessed through paid services. This study attempts to develop an interactive tool for personal literature management based solely on their bibliographic records. To make such a tool possible, we developed a principled “human-in-the-loop latent space learning” method that estimates the management criteria of each researcher based on his or her feedback to calculate the positions of documents in a two-dimensional space on the screen. As a set of bibliographic records forms a graph, our model is naturally designed as a graph-based encoder–decoder model that connects the graph and the space. In addition, we also devised an active learning framework using uncertainty sampling for it. The challenge here is to define the uncertainty in a problem setting. Experiments with ten researchers from the humanities, science, and engineering domains show that the proposed framework provides superior results to a typical graph convolutional encoder–decoder model. In addition, we found that our active learning framework was effective in selecting good samples.

show abstract

Query-Augmented Active Metric Learning

Cited by 6 publications

References 31 publications

HCPC: A New Parsimonious Clustering Method based on Hierarchical Characters for Morphological Phylogenetic Reconstruction

HCPC: A New Parsimonious Clustering Method based on Hierarchical Characters for Morphological Phylogenetic Reconstruction

Bibrecord-Based Literature Management with Interactive Latent Space Learning

Human-in-the-loop latent space learning for biblio-record-based literature management

Contact Info

Product

Resources

About