Learning a discriminative dictionary for classification with outliers

Qi, Jiaming; Chen, Wei

doi:10.1016/j.sigpro.2018.06.005

Cited by 6 publications

(3 citation statements)

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“…A key premise of FS is that the data contains redundant or irrelevant features, and thus removing those features does not result in loss of information in the prediction [46]. Dictionary learning denotes a LIP whose linear operator A and its representation m are learned from the observed data d, which exists in many applications such as image classification [47], outliers detection [48], and distributed CS [49].…”

Section: Lips With Different Structures Of Amentioning

confidence: 99%

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

Bai,

Chen,

Chen

et al. 2020

Preprint

Self Cite

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The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line.

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Section: Lips With Different Structures Of Amentioning

confidence: 99%

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

Bai,

Chen,

Chen

et al. 2020

Preprint

Self Cite

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“…If any data point is extremely different from the whole data in a process, then it is marked as an outlier (Qi & Chen, 2018). Outliers may emerge because of various causes such as malicious attacks, environmental factors, human errors, abnormal conditions, measurement errors, and hardware malfunction.…”

Section: Introductionmentioning

confidence: 99%

A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection

Dogan

Birant

2020

Journal of Data and Information Science

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PurposeThe main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately. To serve this purpose, a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.Design/methodology/approachThis paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems. The proposed approach, named Bagged and Voted Local Outlier Detection (BV-LOF), benefits from the Local Outlier Factor (LOF) as the base algorithm and improves its detection rate by using ensemble methods.FindingsSeveral experiments have been performed on ten benchmark outlier detection datasets to demonstrate the effectiveness of the BV-LOF method. According to the results, the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average.Research limitationsIn the BV-LOF approach, the base algorithm is applied to each subset data multiple times with different neighborhood sizes (k) in each case and with different ensemble sizes (T). In our study, we have chosen k and T value ranges as [1–100]; however, these ranges can be changed according to the dataset handled and to the problem addressed.Practical implicationsThe proposed method can be applied to the datasets from different domains (i.e. health, finance, manufacturing, etc.) without requiring any prior information. Since the BV-LOF method includes two-level ensemble operations, it may lead to more computational time than single-level ensemble methods; however, this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree.Originality/valueThe proposed approach (BV-LOF) investigates multiple neighborhood sizes (k), which provides findings of instances with different local densities, and in this way, it provides more likelihood of outlier detection that LOF may neglect. It also brings many benefits such as easy implementation, improved capability, higher applicability, and interpretability.

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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%

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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Learning a discriminative dictionary for classification with outliers

Cited by 6 publications

References 38 publications

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection

Structured sparse representations for supervised and unsupervised learning

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