Abstract / truncated to 115 words (read the full abstract)

In many modern signal processing applications, traditional machine learning and pattern recognition methods heavily rely on the having a sufficiently large amount of data samples to correctly estimate the underlying structures within complex signals. The main idea is to understand the inherent structural information and relationships embedded within the raw data, thereby enabling a wide variety of inference tasks. Nevertheless, the definition of what constitutes a sufficiently large dataset remains subjective and it is often problem-dependent. In this context, traditional learning approaches often fail to learn meaningful structures in the cases where the number of features closely matches (or even exceeds) the number of observations. These scenarios emphasize the need for tailored strategies that effectively ... toggle 7 keywords

clustering – statistical analysis – riemannian geometry – covariance matrix distance – wireless communication – MIMO – random matrix theory

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