Dictionary Learning for Fast Classification Based on Soft-thresholding

Fawzi, Alhussein; Davies, Mike E.; Frossard, Pascal

doi:10.1007/s11263-014-0784-7

Cited by 33 publications

(42 citation statements)

References 25 publications

(43 reference statements)

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“…Accuracy Fawzi et al [7] 53.44% Coates et al [4] 79.6% Coates et al [5] 81.5% Proposed method 83.03% Table 4. Performance comparison on the CIFAR-10 dataset.…”

Section: Methodsmentioning

confidence: 99%

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Multi-layer Dictionary Learning for Image Classification

Tim¹,

Rombaut²,

Pellerin³

2016

Advanced Concepts for Intelligent Vision Systems

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This paper presents a multi-layer dictionary learning method for classification tasks. The goal of the proposed multi-layer framework is to use the supervised dictionary learning approach locally on raw images in order to learn local features. This method starts by building a sparse representation at the patch-level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. This method has been tested on a classification task with good results.

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“…Accuracy Fawzi et al [7] 53.44% Coates et al [4] 79.6% Coates et al [5] 81.5% Proposed method 83.03% Table 4. Performance comparison on the CIFAR-10 dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Table 4 shows some results on the CIFAR-10 dataset without data augmentation. Fawzi et al [7] use a single layer dictionary learning method for comparison. Coates et al [4], [5] used unsupervised multi-layer sparse coding with large dictionaries (up to 4k atoms).…”

Section: Methodsmentioning

confidence: 99%

Multi-layer Dictionary Learning for Image Classification

Tim¹,

Rombaut²,

Pellerin³

2016

Advanced Concepts for Intelligent Vision Systems

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“…Moreover, Sep-DNAOL enables us to produce a MAP solution of the features with task-adapted regularization in a latent way while features in DPL are the intermediate latent variables obtained with a maximizing likelihood estimation (MLE) so that the inconsistency issue will still appear in their framework. We will also compare NonSep-DNAOL with fast soft-thresholding based dictionary learning (ST-DL) [54]. Intuitively speaking, apart from their distinct motivations, ST-DL can be also viewed as a special instance of NonSep-DNAOL, but the classification loss functions in two frameworks are different.…”

Section: Framework Comparisonmentioning

confidence: 99%

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification

Wen

Hou

Jiao

2017

IEEE Trans. on Image Process.

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A linear synthesis model-based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it, however, suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task-adapted feature transformation and regularization to encode our preferences, domain prior knowledge, and task-oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms, but it can also dramatically reduce the time complexities in both training and testing phases.

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“…Among a few fast sparse coding approximations, the simplest choice is arguably the thresholded feature [9], [13], [18], [34], [44]:z…”

Section: Introductionmentioning

confidence: 99%

Learning Simple Thresholded Features With Sparse Support Recovery

Wang

Yang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard dictionaries often lead to non-optimal performance when used for computing thresholded features. In this paper, we first present two theoretical recovery guarantees for the thresholded feature to exactly recover the nonzero support of the sparse code. Motivated by them, we then formulate the Dictionary Learning for Thresholded Features (DLTF) model, which learns an optimized dictionary for applying the thresholded feature. In particular, for the (k, 2) norm involved, a novel proximal operator with log-linear time complexity O(m log m) is derived. We evaluate the performance of DLTF on a vast range of synthetic and real-data tasks, where DLTF demonstrates remarkable efficiency, effectiveness and robustness in all experiments. In addition, we briefly discuss the potential link between DLTF and deep learning building blocks.

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Dictionary Learning for Fast Classification Based on Soft-thresholding

Cited by 33 publications

References 25 publications

Multi-layer Dictionary Learning for Image Classification

Multi-layer Dictionary Learning for Image Classification

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification

Learning Simple Thresholded Features With Sparse Support Recovery

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