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.
['Kai Ming Ting', 'Takashi Washio', 'Jonathan R. Wells', 'Sunil Aryal']
Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors