TY - GEN

T1 - Partial Multi-label Learning via Constraint Clustering

AU - Siahroudi, Sajjad Kamali

AU - Kudenko, Daniel

N1 - Funding Information: This work has been partially supported by the Volkswagen foundation.

PY - 2023

Y1 - 2023

N2 - Multi-label learning (MLL) refers to a learning task where each instance is associated with a set of labels. However, in most real-world applications, the labeling process is very expensive and time consuming. Partially multi-label learning (PML) refers to MLL where only a part of the labels are correctly annotated and the rest are false positive labels. The main purpose of PML is to learn and predict unseen multi-label data with less annotation cost. To address the ambiguities in the label set, existing popular PML research attempts to extract the label confidence for each candidate label. These methods mainly perform disambiguation by considering the correlation among labels or/and features. However, in PML because of noisy labels, the true correlation among labels is corrupted. These methods can be easily misled by noisy false-positive labels. In this paper, we propose Partial Multi-Label learning method via Constraint Clustering (PML-CC) to address PML based on the underlying structure of data. PML-CC gradually extracts high-confidence labels and then uses them to extract the rest labels. To find the high-confidence labels, it solves PML as a clustering task while considering extracted information from previous steps as constraints. In each step, PML-CC updates the extracted labels and uses them to extract the other labels. Experimental results show that our method successfully tackles PML tasks and outperforms the state-of-the-art methods on artificial and real-world datasets.

AB - Multi-label learning (MLL) refers to a learning task where each instance is associated with a set of labels. However, in most real-world applications, the labeling process is very expensive and time consuming. Partially multi-label learning (PML) refers to MLL where only a part of the labels are correctly annotated and the rest are false positive labels. The main purpose of PML is to learn and predict unseen multi-label data with less annotation cost. To address the ambiguities in the label set, existing popular PML research attempts to extract the label confidence for each candidate label. These methods mainly perform disambiguation by considering the correlation among labels or/and features. However, in PML because of noisy labels, the true correlation among labels is corrupted. These methods can be easily misled by noisy false-positive labels. In this paper, we propose Partial Multi-Label learning method via Constraint Clustering (PML-CC) to address PML based on the underlying structure of data. PML-CC gradually extracts high-confidence labels and then uses them to extract the rest labels. To find the high-confidence labels, it solves PML as a clustering task while considering extracted information from previous steps as constraints. In each step, PML-CC updates the extracted labels and uses them to extract the other labels. Experimental results show that our method successfully tackles PML tasks and outperforms the state-of-the-art methods on artificial and real-world datasets.

KW - constraint clustering

KW - disambiguation

KW - partial multi-label learning

UR - http://www.scopus.com/inward/record.url?scp=85178635833&partnerID=8YFLogxK

U2 - 10.1007/978-981-99-8145-8_35

DO - 10.1007/978-981-99-8145-8_35

M3 - Conference contribution

AN - SCOPUS:85178635833

SN - 9789819981441

T3 - Communications in Computer and Information Science

SP - 453

EP - 469

BT - Neural Information Processing

A2 - Luo, Biao

A2 - Cheng, Long

A2 - Wu, Zheng-Guang

A2 - Li, Hongyi

A2 - Li, Chaojie

PB - Springer Science and Business Media Deutschland GmbH

T2 - 30th International Conference on Neural Information Processing, ICONIP 2023

Y2 - 20 November 2023 through 23 November 2023

ER -