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Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
Content based image retrieval using image features information fusion
Recent image retrieval techniques are focusing on multiple image features for the efficient
image retrieval. It has been an inevitable requirement to fetch the images from a variety of …
image retrieval. It has been an inevitable requirement to fetch the images from a variety of …
Fast low-rank shared dictionary learning for image classification
Despite the fact that different objects possess distinct class-specific features, they also
usually share common patterns. This observation has been exploited partially in a recently …
usually share common patterns. This observation has been exploited partially in a recently …
Discriminative fisher embedding dictionary learning algorithm for object recognition
Both interclass variances and intraclass similarities are crucial for improving the
classification performance of discriminative dictionary learning (DDL) algorithms. However …
classification performance of discriminative dictionary learning (DDL) algorithms. However …
Face recognition via collaborative representation: Its discriminant nature and superposed representation
Collaborative representation methods, such as sparse subspace clustering (SSC) and
sparse representation-based classification (SRC), have achieved great success in face …
sparse representation-based classification (SRC), have achieved great success in face …
Robust visual knowledge transfer via extreme learning machine-based domain adaptation
We address the problem of visual knowledge adaptation by leveraging labeled patterns from
source domain and a very limited number of labeled instances in target domain to learn a …
source domain and a very limited number of labeled instances in target domain to learn a …
A novel dictionary learning named deep and shared dictionary learning for fault diagnosis
H Wang, G Dong, J Chen, X Hu, Z Zhu - Mechanical Systems and Signal …, 2023 - Elsevier
As the core of the Sparseland, dictionary learning has represented excellent performances
in many fields, such as pattern recognition, fault diagnosis, noise reduction, image …
in many fields, such as pattern recognition, fault diagnosis, noise reduction, image …
SDHC: Joint semantic-data guided hierarchical classification for fine-grained HRRP target recognition
High-resolution range profile (HRRP) is increasingly employed in radar target recognition
under intricate ground scenarios. Such scenarios demand recognizing the specific type of a …
under intricate ground scenarios. Such scenarios demand recognizing the specific type of a …
Joint hierarchical category structure learning and large-scale image classification
We investigate the scalable image classification problem with a large number of categories.
Hierarchical visual data structures are helpful for improving the efficiency and performance …
Hierarchical visual data structures are helpful for improving the efficiency and performance …
Hierarchical convolutional neural network with knowledge complementation for long-tailed classification
Existing methods based on transfer learning leverage auxiliary information to help tail
generalization and improve the performance of the tail classes. However, they cannot fully …
generalization and improve the performance of the tail classes. However, they cannot fully …