SQuadMDS: A lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP

Lambert, Pierre; Bodt, Cyril de; Verleysen, Michel; Lee, John A.

doi:10.1016/j.neucom.2022.06.108

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…However, there is utility to imposing constraints on relative distances of points picked irrespective of this close-versus-distant classification. This idea is explored by SQuadMDS (26; 27), and a variant thereof is implemented in ViVAE as the quartet loss (used by the ViVAE -quartet model).…”

Section: Methodsmentioning

confidence: 99%

A framework for quantifiable local and global structure preservation in single-cell dimensionality reduction

Novak,

de Bodt,

Lambert

et al. 2023

Preprint

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Dimensionality reduction techniques are essential in current single-cell ‘omics approaches, offering biologists a first glimpse of the structure present in their data. These methods are most often used to visualise high-dimensional and noisy input datasets, but are also frequently applied for downstream structure learning. By design, every dimensionality reduction technique preserves some characteristics of the original, high-dimensional data, while discarding others. We introduceViScore, a framework for validation of low-dimensional embeddings, consisting of novel quantitative measures and visualisations to assess their quality in both supervised and unsupervised settings. Next, we presentViVAE, a new dimensionality reduction method which uses graph-based transformations and deep learning models to visualise important structural relationships. We demon-strate thatViVAEstrikes a better balance in preserving both local and global structures compared to existing methods, achieving general-purpose visualisation but also facilitating analyses of developmental trajectories.

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Section: Methodsmentioning

confidence: 99%

A framework for quantifiable local and global structure preservation in single-cell dimensionality reduction

Novak,

de Bodt,

Lambert

et al. 2023

Preprint

Self Cite

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“…Through this system, the user's information on various electricity consumption links is collected. Such as low-voltage user centralized copying, distribution substation metering monitoring, and dedicated substation load management and other links (Lambert et al 2022). Among them, distribution transformer metering and monitoring refers to the process of power metering and monitoring of distribution transformers.…”

Section: Household Change Relationship Identification Model Based On ...mentioning

confidence: 99%

Automatic grid topology detection method based on Lasso algorithm and t-SNE algorithm

Huang,

Que,

Zhang

et al. 2024

Energy Inform

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For a long time, the low-voltage distribution network has the problems of untimely management and complex and frequently changing lines, which makes the problem of missing grid topology information increasingly serious. This study proposes an automatic grid topology detection model based on lasso algorithm and t-distributed random neighbor embedding algorithm. The model identifies the household-variable relationship through the lasso algorithm, and then identifies the grid topology of the station area through the t-distributed random neighbor embedding algorithm model. The experimental results indicated that the lasso algorithm, the constant least squares algorithm and the ridge regression algorithm had accuracies of 0.88, 0.80, and 0.71 and loss function values of 0.14, 0.20, and 0.25 for dataset sizes up to 500. Comparing the time spent on identifying household changes in different regions, in Region 1, the training time for the Lasso algorithm, the Constant Least Squares algorithm, and the Ridge Regression algorithm is 2.8 s, 3.0 s, and 3.1 s, respectively. The training time in region 2 is 2.4s, 3.6s, and 3.4s, respectively. The training time in region 3 is 7.7 s, 1.9 s, and 2.8 s, respectively. The training time in region 4 is 3.1 s, 3.6 s, and 3.3 s, respectively. The findings demonstrate that the suggested algorithmic model performs better than the other and can identify the structure of LV distribution networks.

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Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

Wang

Yang

et al. 2023

Nat Commun

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While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

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SQuadMDS: A lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP

Cited by 3 publications

References 27 publications

A framework for quantifiable local and global structure preservation in single-cell dimensionality reduction

A framework for quantifiable local and global structure preservation in single-cell dimensionality reduction

Automatic grid topology detection method based on Lasso algorithm and t-SNE algorithm

Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

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