Dimensionality-bound

Dimensionality-bound describes a system, algorithm, or process whose performance, effectiveness, or efficiency is significantly limited or constrained by the number of dimensions in its input data or the space it operates within. This limitation often arises because increased dimensionality leads to the 'curse of dimensionality,' where data becomes sparse, computational complexity skyrockets, and the relationships between data points become harder to discern. The effectiveness decreases disproportionately to the increase of dimensionality, with the potential to become practically useless or even detrimental beyond a certain point. Solutions frequently involve dimensionality reduction techniques.

Dimensionality-bound meaning with examples

• The image recognition algorithm was Dimensionality-bound, struggling to differentiate complex patterns in high-resolution images with many color channels and spatial features. Its performance degraded rapidly as the number of dimensions increased, making it unsuitable for analyzing extremely large datasets.
• Scientific simulations used to study climate change are often Dimensionality-bound due to the complexity of the systems. Incorporating various atmospheric and oceanic variables greatly increased the number of dimensions, which meant greater computational costs and extended runtimes that limited the breadth of the study.
• A database indexing system, designed for searching through medical records, became Dimensionality-bound when it included an increasingly wide set of symptoms and lab results, making searches inefficient and slow. Performance declined as the number of potential search parameters, or dimensions, increased.
• Machine learning models, which are being used for fraud detection are often dimensionality-bound. When you add many features that describe transaction, the predictive abilities can reduce as the noise of less helpful features drowns out the useful signals; thereby creating inaccurate fraud alerts.
• When using feature selection for a marketing campaign, if too many attributes are incorporated to describe the customer, the model becomes dimensionality-bound. As more customer characteristics (age, location, purchase history etc.) were added, the model struggled to make accurate predictions and the ability to segment its target groups was compromised.