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Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as `more data the better'. We call this `the gravity of learning curve', and it is assumed that no learning algorithms are `gravity-defiant'. Contrary to the conventional wisdom, this paper provides the theoretical analysis and the empirical evidence that nearest neighbour anomaly detectors are gravity-defiant algorithms.

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors