Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.
Efficient Training for Positive Unlabeled Learning / Sansone, Emanuele; De Natale, Francesco; Zhou, Zhi-Hua. - 2018(2018).
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|Titolo:||Efficient Training for Positive Unlabeled Learning|
|Autori:||Sansone, Emanuele; De Natale, Francesco; Zhou, Zhi-Hua|
|Titolo del periodico:||IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE|
|Anno di pubblicazione:||2018|
|Citazione:||Efficient Training for Positive Unlabeled Learning / Sansone, Emanuele; De Natale, Francesco; Zhou, Zhi-Hua. - 2018(2018).|
|Appare nelle tipologie:||03.1 Articolo su rivista (Journal article)|