CORDIS Project
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This project focuses on improving the classification of high-dimensional signals, such as audio and images, by developing a unified mathematical framework. It aims to create more stable and informative representations to reduce classification errors, addressing challenges in signal variability and unsupervised learning…
Considerable amounts of high-dimensional signals are continuously being acquired, whether audio, images, videos, or specialized signals for example in geophysics or medicine.
Automatic classification and retrieval is strongly needed to analyze and access these massive data sets, but current algorithms often produce too many errors.
For high-dimensional signals, supervised classification algorithms are typically applied to reduced ``feature vectors''.
These feature representations are specialized…
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