Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 28

During the last decade, technology has advanced in tremendous ways, and analytics and statistics have played major roles. These techniques fetch an enormous number of datasets that is usually composed of many variables. For instance, the real‐world datasets for image processing, Internet search engines, text analysis, and so on, usually have a higher dimensionality, and to handle such dimensionality, it needs to be reduced but with the requirement that specific information should remain unchanged.

Figure 2.13 k = 3 means clustering on 2D dataset.

Source: Based on PulkitS01 [3], K‐Means implementation, GitHub, Inc. Available at [53],https://gist.github.com/PulkitS01/97c9920b1c913ba5e7e101d0e9030b0e.

Figure 2.14 Concept of data projection.

Dimensionality reduction [32–34] is a method of converting high‐dimensional variables into lower‐dimensional variables without changing the specific information of the variables. This is often used as a preprocessing step in classification methods or other tasks.

Linear dimensionality reduction linearly projects n‐dimensional data onto a k‐dimensional space, k < n, often k < < n (Figure 2.14).