A dimensionality reduction approach to support visual data mining: Co-ranking-based evaluation

Griparis, Andreea; Faur, Daniela; Datcu, Mihai

doi:10.1109/iccomm.2016.7528271

Cited by 3 publications

(1 citation statement)

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“…The dimensionality reduction is a procedure of converting high‐dimensional data into a meaningful representation with lesser dimension 43 . The dimensionality reduction techniques simplify the data so that it can be processed efficiently 44 …”

Section: Dimensionality Reductionmentioning

confidence: 99%

Data dimensionality reduction techniques for Industry 4.0: Research results, challenges, and future research directions

et al. 2020

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Summary From the last few years, we have witnessed the fourth generation industrial revolution (Industry 4.0), impact of which will be seen in the years to come in various disciplines such as healthcare, transportation, IoT, smart grid, autonomous vehicles, and image processing. These applications in Industry 4.0 may have data in the form of images, speech signals, videos having high dimensions containing multiple dimensions to represent data along different axis. So, the complexity of data processing increases with an increase in the dimensions of the dataset. Complexity can be viewed in terms of detecting and exploiting the relationships among different features of the dataset. These complexities among different attributes can be reduced with the help of dimensionality reduction techniques. These techniques reduce the dimensions from the original input dataset to a lower dimensional dataset. Dimensionality reduction methods are broadly categorized into two types as feature extraction and feature selection. In feature selection method, out of the original set, a subset of features are identified to get a smaller subset which can be used to build the model whereas, the feature extraction method reduces the dataset of high dimensions to a lower dimension space, that is, a space with a less number of features having different values in comparison to the original dataset. Keeping focus on these points, in this article, we have compared and analyzed different data dimensionality reduction techniques which reduce the dimensions of a large and complex dataset during data processing. In addition, we have discussed various data dimensionality reduction techniques and compared these techniques with respect to various parameters. The comparison among various techniques provides insights to the readers about the applicability of a specific technique to the stand‐alone or a group of applications.

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Section: Dimensionality Reductionmentioning

confidence: 99%