Overcomplete representation

An overcomplete representation is a way of describing data using more building blocks, or features, than the minimal needed to capture its information. Imagine describing a complex image with many overlapping shapes instead of just a few; this redundancy allows for greater flexibility, robustness, and detail in how the data is represented. In fields like signal processing or machine learning, overcomplete representations help models better capture nuanced patterns and details, leading to improved performance. Essentially, it's a richer, more flexible way to encode information, even if it involves some redundancy.