As datasets grow larger, the quality of nearest neighbors (NN) retrieval can degrade due to the increased likelihood of encountering "impostor" points—data points that appear close to a query in the embedding space but lack meaningful similarity. This occurs because, in high-dimensional spaces, distance metrics like Euclidean or cosine similarity become less reliable as the number of points increases. For example, in a dataset of 1 million images, a query image of a cat might retrieve visually similar but semantically unrelated images (e.g., a dog with similar colors) simply because the sheer volume of data increases the probability of random proximity. This problem intensifies in high dimensions due to the "curse of dimensionality," where distances between points converge, making it harder to distinguish true neighbors from noise.
The risk of impostors is further amplified by the statistical properties of large datasets. In a uniform distribution, the expected distance between a query and its nearest neighbor shrinks as the dataset grows, but the variance in distances also decreases. This means more points cluster around the same average distance, increasing the chance that some will be close purely by chance rather than genuine similarity. For instance, in text retrieval, a search for "machine learning" in a small corpus might reliably return relevant articles, but in a billion-document dataset, many unrelated texts could appear near the query due to overlapping keywords or shared jargon, despite addressing entirely different topics.
Finally, scalability challenges compound these issues. Exact NN methods become computationally infeasible for large datasets, forcing reliance on approximate algorithms like locality-sensitive hashing (LSH) or graph-based indexes. These methods trade accuracy for speed, potentially missing true neighbors while retrieving impostors. For example, a recommendation system using approximate NN might suggest irrelevant products because the algorithm prioritized speed over verifying semantic relevance. Additionally, "hubness"—where certain points act as frequent neighbors for many queries—becomes more pronounced in large datasets, turning hubs into systemic sources of impostors. Mitigating these effects often requires refining distance metrics, incorporating domain-specific constraints, or using hybrid approaches that balance scale with contextual relevance.