Discernible visualization of high dimensional data using label information

Kiyadeh, Asef Pourmasoumi Hasan; Zamiri, Amin; Yazdi, Hadi Sadohgi; Ghaemi, Hadi

doi:10.1016/j.asoc.2014.09.026

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…SCs have been used in literature for decision tree construction to classify different objects [53], perform cluster analysis [54], finding trends for decision making [55], and visualizing linearly separable clusters [56]. However, for our data sets with hundreds of dimensions, it would not be possible to interact with the vectors in an intuitive way.…”

Section: Cluster Analysismentioning

confidence: 99%

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

et al. 2020

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Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.

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Section: Cluster Analysismentioning

confidence: 99%

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

et al. 2020

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“…Baseadas na t-SNE: UTOPIAN (Choo et al, 2013), Cluster Sculptor (Bruneau et al, 2015) e DS t-SNE (Kim et al, 2015). Outros tipos: IRP-Kmeans (Cardoso e Wichert, 2012), Kiyadeh et al (2015) e ReCloud (Wang et al, 2014).…”

Section: Técnicas Para Identificação E Visualização De Agrupamentosmentioning

confidence: 99%

“…No entanto, os testes realizados são superficiais, já que as técnicas comparadas são as mesmas empregadas na construção do próprio IRP-Kmeans. Kiyadeh et al (2015) propuseram um método de visualização semissupervisionada para dados de alta dimensão, requerendo apenas uma fração de dados rotulados. O objetivoé melhorar a visualização e identificação de agrupamentos nos dados.…”

Section: Técnicas Para Identificação E Visualização De Agrupamentosunclassified

Técnicas de projeção para identificação de grupos e comparação de dados multidimensionais usando diferentes medidas de similaridade

Joia¹,

Nonato²

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Semi-supervised regression with label-guided adaptive graph optimization

Zheng,

Zhang,

Yan

et al. 2024

Appl Intell

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Discernible visualization of high dimensional data using label information

Cited by 3 publications

References 35 publications

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

Técnicas de projeção para identificação de grupos e comparação de dados multidimensionais usando diferentes medidas de similaridade

Semi-supervised regression with label-guided adaptive graph optimization

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