Parametric t-Distributed Stochastic Exemplar-Centered Embedding

Min, Martin Renqiang; Guo, Hongyu; Shen, Dinghan

doi:10.1007/978-3-030-10925-7_29

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Although t-SNE is non-parametric, there is a parametric t-SNE version [76], which seeks to overcome this limitation. Nonetheless, it is often difficult to find an optimal configuration of hyperparameters for these models, which in turn yields noisy projections compared to those obtained using the non-parametric version of the algorithm [44,87,93].…”

Section: Visual Exploration Of Multidimensional Datamentioning

confidence: 99%

ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

Sabando

Ulbrich

Selzer

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently process the multidimensional space of features. These underlying calculations often hinder interpretability of results and prevent experts from assessing the impact of individual molecular features on the resulting representations. To provide a solution for scrutinizing such complex data, we introduce ChemVA, an interactive application for the visual exploration of large molecular ensembles and their features. Our tool consists of multiple coordinated views: Hexagonal view, Detail view, 3D view, Table view, and a newly proposed Difference view designed for the comparison of DR projections. These views display DR projections combined with biological activity, selected molecular features, and confidence scores for each of these projections. This conjunction of views allows the user to drill down through the dataset and to efficiently select candidate compounds. Our approach was evaluated on two case studies of finding structurally similar ligands with similar binding affinity to a target protein, as well as on an external qualitative evaluation. The results suggest that our system allows effective visual inspection and comparison of different high-dimensional molecular representations. Furthermore, ChemVA assists in the identification of candidate compounds while providing information on the certainty behind different molecular representations.

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Section: Visual Exploration Of Multidimensional Datamentioning

confidence: 99%

ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

Sabando

Ulbrich

Selzer

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…To obtain consistency in the item positioning, several works [12], [13] proposed the use of deep neural networks to mimic the behavior of t-SNE. As with any trained algorithm with no memory nor update mechanism, the inference results is purely deterministic.…”

Section: Introductionmentioning

confidence: 99%

Index $t$-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings

Candel¹,

Naccache²

2021

Preprint

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t-SNE is an embedding method that the data science community has widely used. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric, therefore two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. An approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding' match. The proposed algorithm has the same complexity than the original t-SNE to embed new items, and a lower one when considering the embedding of a dataset sliced into sub-pieces. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets' dynamics.

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Index t-SNE Based on Sobol Sequence Initialized Harris Hawk Optimization Algorithm

Zhang,

Wei,

Nai

et al. 2023

2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

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Parametric t-Distributed Stochastic Exemplar-Centered Embedding

Cited by 4 publications

References 13 publications

ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

Index $t$-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings

Index t-SNE Based on Sobol Sequence Initialized Harris Hawk Optimization Algorithm

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