Generic Process Visualization Using Parametric t-SNE

Zhu, Wuming; Webb, Zachary; Han, Xianyao; Mao, Kaitian; Sun, Wei; Romagnoli, José A.

doi:10.1016/j.ifacol.2018.09.262

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…To visualize our LDA model, we employ t-SNE plot, as recommended by Genender-Feltheimer for exploratory data analysis [31]. T-distributed Stochastic Neighborhood Embedding (tSNE) is an unsupervised Machine Learning algorithm introduced by Maaten and Hinton [32] to visualize high-dimensional data in a low-dimensional space [33]. Figure 4 displays the t-SNE plot of our LDA model configured with six topics.…”

Section: B Experimental Resultsmentioning

confidence: 99%

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Wijanto,

Widiastuti,

Yong

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Topic modeling has emerged as a successful approach to uncovering topics from textual data. Various topic modeling techniques have been introduced, ranging from traditional algorithms to those based on neural networks. In this research, we explore advanced topic modeling techniques, including BERT-based approaches, to enhance the analysis of scientific articles. We first investigate a widely used Latent Dirichlet Allocation (LDA) model and then explore the capabilities of BERT, to automatically uncover latent topics within scientific papers. The goal of this study is to identify the optimal hyperparameter setting for BERT-based topic modeling of scientific articles. We conduct experiments across several scenarios involving combinations of word embedding, dimension reduction, and clustering methods. The results were analyzed based on the coherence values, average execution time, number of topics generated, visualization through the inter-topic distance map, and the top-N-words of each topic. Our findings suggest that combination of RoBERTa for word embedding, PCA for dimension reduction, and K-Means for clustering yields superior results among the tested scenarios. Further evaluation of BERT-based topic modeling is necessary to validate these findings and explore its applications in various academic and industrial contexts. The implications of these advanced techniques could significantly streamline the process of staying updated with scientific literature, potentially revolutionizing research methodologies across disciplines.

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Section: B Experimental Resultsmentioning

confidence: 99%

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Wijanto,

Widiastuti,

Yong

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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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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