Uncorrelated Group LASSO

Kong, Deguang; Liu, Ji; Liu, Bo; Bao, Xuan

doi:10.1609/aaai.v30i1.10317

Cited by 10 publications

(1 citation statement)

References 25 publications

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“…Since the binarization operation for low-dimensional features RP 𝑇 𝑗 c 𝑗 and binary codes b 𝑗 helps increase the information loss, we consider using the feature sparsity for personalized weight P 𝑗 to select important features. Exclusive group lasso (EGL) [18,19] encourages intra-cluster competition but discourages inter-cluster competition. Inspired by EGL we first impose a 𝑙 2,1 -norm regularization on personalized weight P 𝑗 for pursuing the sparsity of intra-cluster features.…”

Section: Personalized Sparse Hashingmentioning

confidence: 99%

Binary Representation via Jointly Personalized Sparse Hashing

Wang

Chen

Lan

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g. color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, firstly, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our proposed JPSH, we incorporate the proposed PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters, but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. We apply the proposed representation learning algorithm JPSH to the similarity search task. Extensive experiments on four benchmark datasets verify that the proposed JPSH outperforms several state-of-the-art unsupervised hashing algorithms.

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Section: Personalized Sparse Hashingmentioning

confidence: 99%

Binary Representation via Jointly Personalized Sparse Hashing

Wang

Chen

Lan

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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A Framework for Feature Selection to Exploit Feature Group Structures

Perera

Chan

Karunasekera

2020

Advances in Knowledge Discovery and Data Mining

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Filter feature selection methods play an important role in machine learning tasks when low computational costs, classifier independence or simplicity is important. Existing filter methods predominantly focus only on the input data and do not take advantage of the external sources of correlations within feature groups to improve the classification accuracy. We propose a framework which facilitates supervised filter feature selection methods to exploit feature group information from external sources of knowledge and use this framework to incorporate feature group information into minimum Redundancy Maximum Relevance (mRMR) algorithm, resulting in GroupMRMR algorithm. We show that GroupMRMR achieves high accuracy gains over mRMR (up to ∼35%) and other popular filter methods (up to ∼50%). GroupMRMR has same computational complexity as that of mRMR, therefore, does not incur additional computational costs. Proposed method has many real world applications, particularly the ones that use genomic, text and image data whose features demonstrate strong group structures.

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