تقرير
Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction
| العنوان: | Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction |
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| المؤلفون: | Kim, Youngjoo, Telea, Alexandru C., Trager, Scott C., Roerdink, Jos B. T. M. |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our `High-Dimensional Sharpened DR' (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog. Comment: This paper has been accepted for Information Visualization. Copyright may be transferred without notice, after which this version may no longer be accessible |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2110.00317 |
| رقم الأكسشن: | edsarx.2110.00317 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |