Graph-constrained supervised dictionary learning for multi-label classification

Yankelevsky, Yael; Elad, Michael

doi:10.1109/icsee.2016.7806064

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…The following algorithms belong to the second category: the random walk-based MLC algorithms, 16,17,19,20,22,23 the MLC algorithm based on Hilbert-Schmidt independence criterion, 24 and the dictionary learning-based DL-MLC algorithm. 25 Specifically, the graph DL-MLC algorithm 25 maps a training set to a graph model and improves the labelconsistent K-SVD algorithm (LC-KSVD) 37 with adopting the graph Laplacian regularization. The genomescale metabolic model (GSMM) method 24 contains three steps: first, it converts the training and test sets to a weighted undirected graph and describes the graph smoothness through a series of transformations including the adjacency matrix, the real label set of training instances, and the predicted label set of test instances; second, the algorithm describes the consistence of label space with the Hilbert-Schmidt independent criterion; third, it builds the MLC classifier through optimizing the smoothness and consistency.…”

Section: Ptms and Aamsmentioning

confidence: 99%

“…For example, considering the application of graph representation to the MLC methods to cope with the above-mentioned problems. However, little progress has been made with these methods and algorithms [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] especially in the following aspects. (1) Complexity: in order to construct a graph model, a graph-based MLC method must map the instances of an entire training set to graph vertices, with many instances irrelevant to the test instances causing very high requirements for time and space and causing high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Wang

Wan

et al. 2020

International Journal of Distributed Sensor Networks

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The multi-label classification problem occurs in many real-world tasks where an object is naturally associated with multiple labels, that is, concepts. The integration of the random walk approach in the multi-label classification methods attracts many researchers’ sight. One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. In this article, we propose a novel multi-label classification algorithm based on the random walk graph and the K-nearest neighbor algorithm (named MLRWKNN). This method constructs the vertices set of a random walk graph for the K-nearest neighbor training samples of certain test data and the edge set of correlations among labels of the training samples, thus considerably reducing the overhead of time and space. The proposed method improves the similarity measurement by differentiating and integrating the discrete and continuous features, which reflect the relationships between instances more accurately. A label predicted method is devised to reduce the subjectivity of the traditional threshold method. The experimental results with four metrics demonstrate that the proposed method outperforms the seven state-of-the-art multi-label classification algorithms in contrast and makes a significant improvement for multi-label classification.

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Section: Ptms and Aamsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Wang

Wan

et al. 2020

International Journal of Distributed Sensor Networks

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“…The DGRDL algorithm and its extensions to a supervised setting [9], [10] already exhibit very good performance in various applications. Nevertheless, a significant limitation common to all current dictionary learning methods for graph signals (including DGRDL), is their poor scalability to high dimensional data, which is limited by the complexity of the training problem as well as by the use of the large graph Laplacian matrices.…”

Section: Introductionmentioning

confidence: 99%

Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals

Yankelevsky

Elad

2019

IEEE Trans. Signal Process.

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Modern data introduces new challenges to classic signal processing approaches, leading to a growing interest in the field of graph signal processing. A powerful and well established model for real world signals in various domains is sparse representation over a dictionary, combined with the ability to train the dictionary from signal examples. This model has been successfully applied to graph signals as well by integrating the underlying graph topology into the learned dictionary. Nonetheless, dictionary learning methods for graph signals are typically restricted to small dimensions due to the computational constraints that the dictionary learning problem entails, and due to the direct use of the graph Laplacian matrix. In this paper, we propose a dictionary learning algorithm that applies to a broader class of graph signals, and is capable of handling much higher dimensional data. We incorporate the underlying graph topology both implicitly, by forcing the learned dictionary atoms to be sparse combinations of graph-wavelet functions, and explicitly, by adding direct graph constraints to promote smoothness in both the feature and manifold domains. The resulting atoms are thus adapted to the data of interest while adhering to the underlying graph structure and possessing a desired multi-scale property. Experimental results on several datasets, representing both synthetic and real network data of different nature, demonstrate the effectiveness of the proposed algorithm for graph signal processing even in high dimensions.

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“…Second, discriminative regularizations can be applied on the sparse coding vectors which are directly responsible for classification. The sparse codes can be regularized via class discrimination [20], [51], label consistency [18], graph regularization [21], [23], support discrimination [25], [52], [53],…”

Section: Objectives and Contributionsmentioning

confidence: 99%

“…A common approach is to transform the coding vectors into their class labels with a linear regressor, and incorporate the linear prediction loss into the reconstruction loss [20], [51]. It is also favorable to require the coding vectors to be similar via different regularizations such as manifold regularization [21], [23], Fisher criterion [47], [202], distance of pairwise support coding vectors [24], etc.…”

Section: Background and Motivationsmentioning

confidence: 99%

Structured sparse representations for supervised and unsupervised learning

Zeng¹

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The technique is particularly important to structural inputs such as images (natural or hyper-spectral), since the convolution operator is shift-invariant and does not break the spatial configurations. This part of the thesis is particularly dedicated to exploring online methods for efficient CDL algorithms where input signals are provided in a streaming fashion to update the dictionary, because the batch mode CDL is time and memory-consuming in nature.The methodology adopted in this part is a systematic extension of classical unsupervised online sparse coding algorithms to the convolutional sparse coding domain, with efficient optimizations achieved via a localized "slice-based" representation of sparse features. Specifically, a slice-based online convolutional dictionary learning (SOCDL) method is proposed for unsupervised image processing tasks such as image reconstruction and inpainting. The expected reconstruction cost with respect to the dictionary is approximated via a surrogate function to enable online learning, and under the slice-based representation a second-order stochastic approximation approach is proposed for optimization. The approximation efficiently generates a dictionary sequence which converges favorably with desired characteristics for image reconstruction. Convergence analysis is presented with theoretical justifications, and the computational complexity is among the lowest within the context of online CDL algorithms with least memory consumptions. Finally, this thesis proposes a novel masked version of SOCDL which can perform online learning on incomplete data for image inpainting.

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Graph-constrained supervised dictionary learning for multi-label classification

Cited by 5 publications

References 22 publications

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals

Structured sparse representations for supervised and unsupervised learning

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