Positive unlabeled learning with tensor networks

Abstract

Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not generate new positive and negative samples. This work introduces a feature-space distance-based tensor network approach to the positive unlabeled learning problem. The presented method is not domain specific and significantly improves the state-of-the-art results on the MNIST image and 15 categorical/mixed datasets. The trained tensor network model is also a generative model and enables the generation of new positive and negative instances.

Keywords

pozitivno neoznačeno učenje;tenzorske mreže;matrično produktni nastavek;positive unlabeled learning;tensor networks;matrix product states;

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004
COBISS:	177078019
ISSN:	0925-2312
Views:	15
Downloads:	4
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	pozitivno neoznačeno učenje;tenzorske mreže;matrično produktni nastavek;
Type (COBISS):	Article
Pages:	str. 1-11
Volume:	ǂVol. ǂ552
Issue:	[article no.] 126556
Chronology:	Oct. 2023
DOI:	10.1016/j.neucom.2023.126556
ID:	21736159