Title

Visualization for solving non-image problems and Saliency Mapping

Document Type

Oral Presentation

Campus where you would like to present

Ellensburg

Event Website

https://digitalcommons.cwu.edu/source

Start Date

18-5-2020

Abstract

Integration between visualization, visual analytics, machine learning, and data mining are the key aspects of data science research. This project proposes a new CPC-R algorithm used to convert non-images into images by visualizing data using paired coordinates. Powerful deep learning algorithms open an opportunity and solve the problem of transforming non-image machine learning problems into image recognition. The main idea of CPC is splitting attributes of an n-D point to consecutive pairs of its attributes. High-dimensional data play an important role in knowledge discovery. This experiment is performed by using the Ionosphere and Glass datasets from the UCI machine learning repository. Reported the results obtained in the computational experiments with Ionosphere and Glass data with CPC-R for different CNN architectures, and a different number of pixels per cell, which represents each pair of attributes. The Accuracies for the Ionosphere and Glass dataset are 94.44% (2 classes and 34 dimensions). and 95.90% (6 classes and 10 dimensions). The second technique for this project is Saliency Mapping. The saliency models take an input test image and generate a saliency map that predicts which regions of the image will be most likely to draw a human viewer’s attention. The efficiency of the CPC-R algorithm is tested, and further optimization needs to be performed.

Faculty Mentor(s)

Boris Kovalerchuk

Department/Program

Computer Sciences

Additional Mentoring Department

https://cwu.studentopportunitycenter.com/2020/04/visualization-for-solving-non-image-problems-and-saliency-mapping/

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May 18th, 12:00 PM

Visualization for solving non-image problems and Saliency Mapping

Ellensburg

Integration between visualization, visual analytics, machine learning, and data mining are the key aspects of data science research. This project proposes a new CPC-R algorithm used to convert non-images into images by visualizing data using paired coordinates. Powerful deep learning algorithms open an opportunity and solve the problem of transforming non-image machine learning problems into image recognition. The main idea of CPC is splitting attributes of an n-D point to consecutive pairs of its attributes. High-dimensional data play an important role in knowledge discovery. This experiment is performed by using the Ionosphere and Glass datasets from the UCI machine learning repository. Reported the results obtained in the computational experiments with Ionosphere and Glass data with CPC-R for different CNN architectures, and a different number of pixels per cell, which represents each pair of attributes. The Accuracies for the Ionosphere and Glass dataset are 94.44% (2 classes and 34 dimensions). and 95.90% (6 classes and 10 dimensions). The second technique for this project is Saliency Mapping. The saliency models take an input test image and generate a saliency map that predicts which regions of the image will be most likely to draw a human viewer’s attention. The efficiency of the CPC-R algorithm is tested, and further optimization needs to be performed.

https://digitalcommons.cwu.edu/source/2020/COTS/48