The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Yixian Fang - One of the best experts on this subject based on the ideXlab platform.
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semantic enhanced discrete matrix factorization hashing for heterogeneous modal matching
Knowledge Based Systems, 2020Co-Authors: Yixian Fang, Yuwei Ren, Ju H ParkAbstract:Abstract The simultaneous hash representation of heterogeneous modalities has shown its excellent performance in the multi-modal learning community. However, the binary discrete constraint of hash brings a great challenge to the common hash representation of heterogeneous modal data. To resolve the issue, this paper proposes a novel discrete hash method, referred to as Semantic enhanced Discrete Matrix Factorization Hashing (SDMFH). SDMFH directly extracts the common discrete hash representation of all modalities from the reconstructed semantic intermodal Similarity Graph, which makes the hash codes more discriminative. Meanwhile, the semantic labels are regressed to the extracted discrete hash book, so as to further strengthen the discriminating ability of the learned discrete hash book. Moreover, a linear embedding from the kernel space of the original data to the Hamming space is explored to generate the common hash codes of each modality to ensure the consistency of the hash codes of heterogeneous modalities. More importantly, we develop an efficient discrete iterative algorithm based on Stiefel manifold that can directly learn the discrete hash book in a closed form, thus both avoiding quantization loss caused by discrete relaxation and reducing computational complexity. Experimental results on three benchmark data sets show that SDMFH performs better than several state-of-the-art methods for heterogeneous modal matching tasks.
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unsupervised cross modal retrieval via multi modal Graph regularized smooth matrix factorization hashing
Knowledge Based Systems, 2019Co-Authors: Yixian Fang, Huaxiang Zhang, Yuwei RenAbstract:Abstract The existing cross-modal hashing methods often encounter quantization loss which is caused by relaxing discrete hash codes in the process of cross-modal retrieval. To counter this problem, a Multi-modal Graph regularized Smooth matrix Factorization Hashing (MSFH) approach is represented for unsupervised cross-modal retrieval. In the proposal framework, a smooth matrix generated by a control parameter is introduced into the matrix decomposition model, which can guarantee the sparsity of the dictionaries learned and the extracted common features at the same time, thus reducing the quantization loss in the hashing process. Furthermore, to preserve the topology of the original data, a multi-modal Graph regularization term is drawn into the model, which consists of two parts. One is the intra-modal Similarity Graph which is used to preserve the geometric structure of each modality. The other is the inter-modal Similarity Graph reconstructed by the symmetric nonnegative matrix factorization, which is employed to soften the structure difference between modalities. The goal of MSFH is to learn unified hash-codes for multi-modal data in a shared latent semantic space in which the Similarity of different modalities can be estimated effectively. And the corresponding experimental results on three benchmark data sets demonstrate the superiority of the proposed approach over several state-of-the-art cross-modality hashing approaches.
Yuwei Ren - One of the best experts on this subject based on the ideXlab platform.
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semantic enhanced discrete matrix factorization hashing for heterogeneous modal matching
Knowledge Based Systems, 2020Co-Authors: Yixian Fang, Yuwei Ren, Ju H ParkAbstract:Abstract The simultaneous hash representation of heterogeneous modalities has shown its excellent performance in the multi-modal learning community. However, the binary discrete constraint of hash brings a great challenge to the common hash representation of heterogeneous modal data. To resolve the issue, this paper proposes a novel discrete hash method, referred to as Semantic enhanced Discrete Matrix Factorization Hashing (SDMFH). SDMFH directly extracts the common discrete hash representation of all modalities from the reconstructed semantic intermodal Similarity Graph, which makes the hash codes more discriminative. Meanwhile, the semantic labels are regressed to the extracted discrete hash book, so as to further strengthen the discriminating ability of the learned discrete hash book. Moreover, a linear embedding from the kernel space of the original data to the Hamming space is explored to generate the common hash codes of each modality to ensure the consistency of the hash codes of heterogeneous modalities. More importantly, we develop an efficient discrete iterative algorithm based on Stiefel manifold that can directly learn the discrete hash book in a closed form, thus both avoiding quantization loss caused by discrete relaxation and reducing computational complexity. Experimental results on three benchmark data sets show that SDMFH performs better than several state-of-the-art methods for heterogeneous modal matching tasks.
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unsupervised cross modal retrieval via multi modal Graph regularized smooth matrix factorization hashing
Knowledge Based Systems, 2019Co-Authors: Yixian Fang, Huaxiang Zhang, Yuwei RenAbstract:Abstract The existing cross-modal hashing methods often encounter quantization loss which is caused by relaxing discrete hash codes in the process of cross-modal retrieval. To counter this problem, a Multi-modal Graph regularized Smooth matrix Factorization Hashing (MSFH) approach is represented for unsupervised cross-modal retrieval. In the proposal framework, a smooth matrix generated by a control parameter is introduced into the matrix decomposition model, which can guarantee the sparsity of the dictionaries learned and the extracted common features at the same time, thus reducing the quantization loss in the hashing process. Furthermore, to preserve the topology of the original data, a multi-modal Graph regularization term is drawn into the model, which consists of two parts. One is the intra-modal Similarity Graph which is used to preserve the geometric structure of each modality. The other is the inter-modal Similarity Graph reconstructed by the symmetric nonnegative matrix factorization, which is employed to soften the structure difference between modalities. The goal of MSFH is to learn unified hash-codes for multi-modal data in a shared latent semantic space in which the Similarity of different modalities can be estimated effectively. And the corresponding experimental results on three benchmark data sets demonstrate the superiority of the proposed approach over several state-of-the-art cross-modality hashing approaches.
Ju H Park - One of the best experts on this subject based on the ideXlab platform.
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semantic enhanced discrete matrix factorization hashing for heterogeneous modal matching
Knowledge Based Systems, 2020Co-Authors: Yixian Fang, Yuwei Ren, Ju H ParkAbstract:Abstract The simultaneous hash representation of heterogeneous modalities has shown its excellent performance in the multi-modal learning community. However, the binary discrete constraint of hash brings a great challenge to the common hash representation of heterogeneous modal data. To resolve the issue, this paper proposes a novel discrete hash method, referred to as Semantic enhanced Discrete Matrix Factorization Hashing (SDMFH). SDMFH directly extracts the common discrete hash representation of all modalities from the reconstructed semantic intermodal Similarity Graph, which makes the hash codes more discriminative. Meanwhile, the semantic labels are regressed to the extracted discrete hash book, so as to further strengthen the discriminating ability of the learned discrete hash book. Moreover, a linear embedding from the kernel space of the original data to the Hamming space is explored to generate the common hash codes of each modality to ensure the consistency of the hash codes of heterogeneous modalities. More importantly, we develop an efficient discrete iterative algorithm based on Stiefel manifold that can directly learn the discrete hash book in a closed form, thus both avoiding quantization loss caused by discrete relaxation and reducing computational complexity. Experimental results on three benchmark data sets show that SDMFH performs better than several state-of-the-art methods for heterogeneous modal matching tasks.
Ron Shamir - One of the best experts on this subject based on the ideXlab platform.
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a clustering algorithm based on Graph connectivity
Information Processing Letters, 2000Co-Authors: Erez Hartuv, Ron ShamirAbstract:We have developed a novel algorithm for cluster analysis that is based on Graph theoretic techniques. A Similarity Graph is defined and clusters in that Graph correspond to highly connected subGraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a solution with some provably good properties and performs well on simulated and real data.
Bin Wang - One of the best experts on this subject based on the ideXlab platform.
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embedding learning on spectral spatial Graph for semisupervised hyperspectral image classification
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Jiayan Cao, Bin WangAbstract:Scarcity of labeled samples is the main obstacle for hyperspectral image classification tasks when labeling data is considerably costly and time-consuming in real-world scenarios. To alleviate any underfitting problem that may occur due to lack of training data, semisupervised classification frameworks explore the intrinsic information of unlabeled samples and bridge labeled and unlabeled data. In this letter, we propose a novel framework that learns underlying manifold representation and semisupervised classifier simultaneously. It avoids explicit eigenvector decomposition and directly samples via iterating random walk on the Similarity Graph, which makes it feasible to implement on huge Graphs. To verify the efficacy of embedding the learning process, we compare the proposed method with other dimensionality reduction and manifold-learning-based approaches. Experimental results show that compared to the methods using traditional semisupervised strategies, the Graph embedding method gives a better result.