An interactive tool to data analysis visualization techniques.
Broadly, the area of dimensional reduction (DR) is aimed at providing ways to harness high dimensional (HD) information through the generation of lower dimensional (LD) representations, by following a certain data-structure-preservation criterion. In literature there have been reported dozens of DR...
Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
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| Μορφή: | bachelorThesis |
| Γλώσσα: | eng |
| Έκδοση: |
2020
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| Θέματα: | |
| Διαθέσιμο Online: | http://repositorio.yachaytech.edu.ec/handle/123456789/264 |
| Ετικέτες: |
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Προσθήκη ετικέτας | Περίληψη: | Broadly, the area of dimensional reduction (DR) is aimed at providing ways to harness high dimensional (HD) information through the generation of lower dimensional (LD) representations, by following a certain data-structure-preservation criterion. In literature there have been reported dozens of DR techniques, which are commonly used as a preprocessing stage within exploratory data analyses for either machine learning or information visualization (IV) purposes. Nonetheless, the selection of a proper method is a nontrivial and -very often- toilsome task. In this sense, a readily and natural way to incorporate an expert’s criterion into the analysis process, while making this task more tractable is the use of interactive IV approaches. Regarding the incorporation of experts’ prior knowledge there still exists a range of open issues. In this degree thesis, we introduce a here-named Inverse Data Visualization Framework (IDVF), which is an initial approach to make the in-put prior knowledge directly interpretable. Our framework is based on 2D-scatter-plots visuals and spectral kernel-driven DR techniques. To capture either the user’s knowledge or requirements, users are requested to provide changes or movements of data points in such a manner that resulting points are located where best convenient according to the user’s criterion. Next, following a Kernel Principal Component Analysis approach and a mixture of kernel matrices, our framework accordingly estimates an approximate LD space. Then, the rationale behind the proposed IDVF is to adjust as accurately as possible the resulting LD space to the representation while fulfilling users’ knowledge and requirements. Results are greatly promising and open the possibility to novel DR-based visualizations. |
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