Data visualization is one of the techniques to understand the patterns of data in graphical methods. Data visualization is an effective tool for transforming raw data into actionable insights and facilitating data-driven decision-making. High-dimensional synthetic data are datasets created artificially with an abundance of attributes or aspects. This type of synthetic data is particularly helpful for attempting to assess machine learning algorithms and data analysis techniques in scenarios with a large number of factors. The method can be difficult because of the more complicated nature of high-dimensional data, but it is necessary for a variety of applications, such as testing machine learning algorithms, evaluating data analysis techniques, and exploring model behaviour in high-dimensional spaces. These trained high-dimensional synthetic data are given to the visualization techniques to produce graphical representation and better decision-making models. This chapter elaborates on visualizing synthetic high-dimensional data for better understanding by common men.
TopLiterature Review
Vallejos, C.A. (2019). identifies the drawbacks of the dimensionality reduction techniques now in use, such as t-SNE, pointing out problems with data structure preservation, sensitivity to noise, and processing costs and uses an enhanced nonlinear technique for dimensionality reduction called PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding), which overcomes the drawbacks of previous approaches.
Tang, L. (2020). Says Effective analysis and visualization of high-dimensional data is hampered by its dimensions. Information loss frequently results from reducing data dimensionality for visualization. - Handling sizable datasets and noise makes analysis more difficult. And proposes PHATE (Potential of Heat Diffusion for Affinity-based Transition Embedding) PHATE uses potential distances to measure global links and encodes local data structures. - Lower-dimensional representations are created by applying multidimensional scaling (MDS), which takes into account both local and global data structures.
Moon, K.R., van Dijk, D., Wang, Z., et al. (2019) identifies High-dimensional data produced by high-throughput technology need visualization tools that can clearly and concisely represent data structure and trends and Presenting PHATE, a new approach to data visualization that may be used to visualize both global and local nonlinear systems. For PHATE to provide meaningful visualizations, there must be an information-geometric distance between each data point.
Li, Y., Chai, Y., Yin, H., et al. (2021) say Classification relies heavily on effective feature extraction, and although dictionary learning techniques perform poorly on high-dimensional datasets that hide discriminative information, deep learning techniques frequently require large amounts of training data and an Adaptive dictionary learning in a low-dimensional space combined with classification-guided optimization yields discriminative low-dimensional features in a unique feature learning framework for high-dimensional data classification.