What is a significant drawback of using high-dimensional vector spaces?

The significant drawback of using high-dimensional vector spaces is the increased computational cost for searching and processing. As the number of dimensions increases, the complexity of algorithms used for searching and processing also tends to rise. This is a result of the curse of dimensionality, which implies that as the dimensions increase, the volume of the space increases exponentially, making data points more sparse. Consequently, simple distance metrics like Euclidean distance become less meaningful, requiring more sophisticated and computationally expensive algorithms to maintain accuracy in search results.

Additionally, higher-dimensional spaces may lead to longer time complexities during search operations, increased processing time in data retrieval, and greater resource consumption, such as higher memory and processing power requirements. As such, this proliferation of computational demands emphasizes the challenge in efficiently handling and querying high-dimensional data in vector representation systems.