Sparse Coding for Classification via Discrimination Ensemble

Quan, Yuhui; Xu, Yong; Sun, Yeping; Huang, Yan; Gao, Hui

doi:10.1109/cvpr.2016.629

Cited by 43 publications

(30 citation statements)

References 38 publications

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“…Previous research showed that supervised ensemble classifier tend to be more accurate than the individual classifiers that make them up (Džeroski and Ženko, 2004 ; Quan et al, 2016 ). There are many advantages of the ensemble learning.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Soussia

Rekik

2018

Front. Neuroinform.

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Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both low-order and high-order levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres.

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Section: Methodsmentioning

confidence: 99%

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Soussia

Rekik

2018

Front. Neuroinform.

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“…where v i ∈ V is one of the basis vectors in the first dictionary (λ=0. 35), as shown in Figure 2, s 2 =2. The n th Dictionary Learning Layer.…”

Section: The Proposed Ddlcnmentioning

confidence: 76%

Deep Micro-Dictionary Learning and Coding Network

Tang

Wei²,

Wei

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional layers are replaced by novel compound dictionary learning and coding layers. The dictionary learning layer learns an over-complete dictionary for the input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Next, the activated dictionary atoms are assembled together and passed to the next compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the finegrained components which are shared among the input dictionary atoms. In this way, a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare the proposed DDLCN with several dictionary learning methods and deep learning architectures. The experimental results on four popular benchmark datasets demonstrate that the proposed DDLCN achieves competitive results compared with state-of-the-art approaches.ing methods focus on extracting semantic features via a deep network. So far most studies in dictionary learning employ a shallow (single layer) architecture, e.g., currently popular dictionary learning techniques are K-SVD [1], Discriminative K-SVD (D-KSVD) [57] and Label Consistent K-SVD (LC-KSVD) [20] which decompose the training data into a dense basis and sparse coefficients. In addition, both Local Coordinate Coding (LCC) [55, 54] and arXiv:1809.04185v2 [cs.CV]

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“…Task-driven discriminative dictionary learning was introduced in the seminal work of Mairal et al [24] and couples the process of dictionary learning and classifier training, thus incorporating supervised learning to sparse coding. Since then discriminative dictionary learning has enjoyed many successes in diverse areas such as handwritten digit classification [22,39], face recognition [10,39,26], object category recognition [10,26,5], scene classification [5,19,26], and action classification [26].…”

Section: Related Workmentioning

confidence: 99%

End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding

Mavroudi

Bhaskara

Sefati

et al. 2018

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Fine-grained action segmentation and recognition is an important yet challenging task. Given a long, untrimmed sequence of kinematic data, the task is to classify the action at each time frame and segment the time series into the correct sequence of actions. In this paper, we propose a novel framework that combines a temporal Conditional Random Field (CRF) model with a powerful frame-level representation based on discriminative sparse coding. We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level action primitives. This results in a CRF model that is driven by sparse coding features obtained using a discriminative dictionary that is shared among different actions and adapted to the task of structured output learning. We evaluate our method on three surgical tasks using kinematic data from the JIGSAWS dataset, as well as on a food preparation task using accelerometer data from the 50 Salads dataset. Our results show that the proposed method performs on par or better than state-of-the-art methods.

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Sparse Coding for Classification via Discrimination Ensemble

Cited by 43 publications

References 38 publications

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Deep Micro-Dictionary Learning and Coding Network

End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding

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