TY - JOUR
T1 - Multi-view multi-label learning with sparse feature selection for image annotation
AU - Zhang, Yongshan
AU - Wu, Jia
AU - Cai, Zhihua
AU - Yu, Philip S.
PY - 2020/11
Y1 - 2020/11
N2 - In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the special data characteristic, it is hard to perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method, which exploits both view relations and label correlations to select discriminative features for further learning. Specifically, the multi-labeled information is decomposed into a reduced latent label representation to capture higher level concepts and correlations among multiple labels. Multiple local geometric structures are constructed to exploit visual similarities and relations for different views. By taking full advantage of the latent label representation and multiple local geometric structures, the sparse regression model with an l2,1 -norm and an Frobenius norm (F-norm) penalty terms is utilized to perform hierarchical feature selection, where the F-norm penalty performs high-level (i.e., view-wise) feature selection to preserve the informative views and the l2,1 -norm penalty conducts low-level (i.e., row-wise) feature selection to remove noisy features. To solve the proposed formulation, we also devise a simple yet efficient iterative algorithm. Experiments and comparisons on real-world image datasets demonstrate the effectiveness and potential of MSFS.
AB - In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the special data characteristic, it is hard to perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method, which exploits both view relations and label correlations to select discriminative features for further learning. Specifically, the multi-labeled information is decomposed into a reduced latent label representation to capture higher level concepts and correlations among multiple labels. Multiple local geometric structures are constructed to exploit visual similarities and relations for different views. By taking full advantage of the latent label representation and multiple local geometric structures, the sparse regression model with an l2,1 -norm and an Frobenius norm (F-norm) penalty terms is utilized to perform hierarchical feature selection, where the F-norm penalty performs high-level (i.e., view-wise) feature selection to preserve the informative views and the l2,1 -norm penalty conducts low-level (i.e., row-wise) feature selection to remove noisy features. To solve the proposed formulation, we also devise a simple yet efficient iterative algorithm. Experiments and comparisons on real-world image datasets demonstrate the effectiveness and potential of MSFS.
KW - Feature selection
KW - image annotation
KW - multi-label learning
KW - multi-view learning
KW - sparse learning
UR - http://www.scopus.com/inward/record.url?scp=85095718847&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DE200100964
U2 - 10.1109/TMM.2020.2966887
DO - 10.1109/TMM.2020.2966887
M3 - Article
AN - SCOPUS:85095718847
SN - 1520-9210
VL - 22
SP - 2844
EP - 2857
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
IS - 11
ER -