<i>ClustMe</i>: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns

Abbas, Mostafa M.; Aupetit, Michaël; Sedlmair, Michael; Bensmail, Halima

doi:10.1111/cgf.13684

Cited by 36 publications

(87 citation statements)

References 64 publications

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“…Examples are Normalized Stress [7], Trustworthiness and Continuity [24], and Distance Consistency (DSC) [25]. More recently, ClustMe [26] was proposed as a perception-based measure that ranks scatterplots based on cluster-related patterns. While this might be useful for quick overviews or automatic selection of projections, a single score fails to capture more intricate details, such as where and why a projection is good or bad [27].…”

Section: Quality Assessmentmentioning

confidence: 99%

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Chatzimparmpas

Martins

Kerren

2020

IEEE Trans. Visual. Comput. Graphics

127

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t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.

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Section: Quality Assessmentmentioning

confidence: 99%

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Chatzimparmpas

Martins

Kerren

2020

IEEE Trans. Visual. Comput. Graphics

127

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“…First, projection quality metric can be used to assess the quality of each projection by using PCA or t-SNE. This includes global measures such that Normalized Stress, Distance Consistency, ClustMe [85] , or local measures such as projection precision score [86] can contribute to shedding light on the quality of such projections. Nevertheless, it should be noted that such assessment may also be misleading and cannot contribute towards comprehending why such results occurred .…”

Section: Experiments and Resultsmentioning

confidence: 99%

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

Muhammad

Hoque

Oussalah

et al. 2022

Knowledge-Based Systems

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COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.

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“…Therefore, papers such as those by Bernard et al [BHZ*18, BZSA18], Gang et al [GRM10], and Kucher et al [KPSK17], although undoubtedly interesting, are out of the scope of our survey, since their research contributions are exclusively based on labeling data. Other partially‐related papers [AASB19, AW12, BHGK14, FBT*10, SBTK08, ZSCC18] are also not included because they focus on using clustering solely to explore the data, without addressing inherent problems of the method. For similar reasons, the paper by Wenskovitch et al [WCR*18], that tries to connect and aggregate benefits from clustering and DR methods, was excluded.…”

Section: Methodology Of the Literature Searchmentioning

confidence: 99%

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas

Martins

Jusufi

et al. 2020

Computer Graphics Forum

143

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Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

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ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns

Cited by 36 publications

References 64 publications

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

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