Analysis Dictionary Learning: an Efficient and Discriminative Solution

Tang, Wen; Panahi, Ashkan; Krim, Hamid; Dai, Liyi

doi:10.1109/icassp.2019.8683687

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…We mainly evaluate our CF-SECL method for pattern classification. The performance of our method is compared with that of some related state-of-the-art methods: KSVD [33], D-KSVD [12], LC-KSVD2 [13], FDDL [7], [8], DPL [9], JEDL [14], SRC [1], DADCL [19], DCADL [16], TL-FC [17], and DNAOL [18]. Our training model has three parameters i.e., α, β, and γ ) to estimate.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, DPL, FDDL learn dictionary for sparse coding of training samples; DSRC and JNPDL jointly learn projection and dictionary for discriminative sparse coding of training samples; while SRC and our method use the training sample as synthesis dictionary for sparse coding of training samples. Similar to D-KSVD [12], LC-KSVD [13], JEDL [14], DCADL [16], TL-FC [17], DNAOL [18], our method applies the learnt linear classifier to the sparse feature to obtain the label of a test sample. However, there are differences in the training models.…”

Section: B Classification-friendly Sparse Encoder and Classifier Leamentioning

confidence: 99%

“…Note that this sparse code autoextractor can be taken as the analysis dictionary and we will call it sparse encoder in our method. The method DCADL [16] learns a discriminative convolutional analysis dictionary and a linear classifier. It essentially uses the convolutional analysis dictionary as a sparse encoder to map the input data into their sparse codes, which are then mapped to the labels by the learnt classifier.…”

Section: Introductionmentioning

confidence: 99%

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Classification-Friendly Sparse Encoder and Classifier Learning

et al. 2020

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Sparse representation (SR) and dictionary learning (DL) have been extensively used for feature encoding, aiming to extract the latent classification-friendly feature of observed data. Existing methods use sparsity penalty and learned dictionary to enhance discriminative capability of sparse codes. However, training dictionary for SR is time consuming and the resulted discriminative capability is limited. Rather than learning dictionary, we propose to employ the dictionary at hand, e.g., the training set as the classspecific synthesis dictionary to pursue an ideal discriminative property of SR of the training samples: each data can be represented only by data-in-class. In addition to the discriminative property, we also introduce a smoothing term to enforce the representation vectors to be uniform within class. The discriminative property helps to separate the data from different classes while the smoothing term tends to group the data from the same class and further strengthen the separation. The SRs are used as new features to train a sparse encoder and a classifier. Once the sparse encoder and the classifier are learnt, the test stage is very simple and highly efficient. Specifically, the label of a test sample can be easily computed by multiplying the test sample with the sparse encoder and the classifier. We call our method Classification-Friendly Sparse Encoder and Classifier Learning (CF-SECL). Extensive experiments show that our method outperforms some state-of the-art model-based methods.

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Section: Resultsmentioning

confidence: 99%

Section: B Classification-friendly Sparse Encoder and Classifier Leamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification-Friendly Sparse Encoder and Classifier Learning

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“…The idea is based on decomposing the input data set into meaningful features and picking a few of them for decision making. In order to obtain the elements of dictionary, the principle components analysis (PCA) is used [16]. The elements of dictionary are human interpretable.…”

Section: Explainable Convolutional Neural Networkmentioning

confidence: 99%

Signal parameter estimation and classification using mixed supervised and unsupervised machine learning approaches

2020

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The increasing use of modern power electronics raises the issue of harmonics in power systems which ultimately deteriorate its optimal performance in terms of: increased power loss, breaker failure and mal-operation of equipment. It has been found that the most severe harmonics in the system are odd ones due to their unsymmetrical nature. This work presents the new framework for estimation and classification of harmonics using machine learning approaches. Initially, a shallow neural network and fuzzy logic systems are used to estimate the harmonics contents in the voltage and currents signals. Based on the sequence components and IHD level of source signals, the estimation of harmonic content is achieved. The obtained results are compared with the analytically computed data for validating the performance of designed networks. The results from neural and fuzzy systems are then used to train the explainable convolutional neural network (xCNN) for harmonics classification. The xCNN consists of pertained ALEXNET network which trains the standard binary support vector machine (SVM) for classification of harmonics. The dictionary-based approach is used to add the explanations to the SVM classifier output as a prototype. The performance of proposed framework is measured in-terms of accuracy and loss function and evaluated on the basis of its scalability and computability. The proposed approach is called a Human with Machine-In-Loop (HMIL). INDEX TERMS Power quality (PQ), individual harmonics distortion (IHD), neural network (NN), fuzzy inference system (FIS), explainable convolution neural network (xCNN)

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“…shown excellent performance in image classification tasks, such as [31] and [32]. It is worth noting that ADL is often applied to image representation vectors to acquire more discriminative vectors.…”

Section: Related Workmentioning

confidence: 99%

A Category-Specific Dictionary Learning Method Tailored for Reconstruction-Based Feature Coding

Duan

et al. 2020

IEEE Access

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Bag-of-Visual-Words (BoVW) is still a useful image classification model when there is not enough data to use Deep Learning. In BoVW model, the practice of reducing the reconstruction errors of local features can improve the classification accuracy owing to the decrease of information loss. Many reconstruction-based coding methods are proposed to learn a visual dictionary and encode local features via minimizing the reconstruction errors of local features with constraints. Besides this, the accuracy can also be improved by learning the category-specific dictionaries and then encoding features based on these dictionaries. By considering the two practices together, we propose a simple category-specific dictionary learning method tailored for reconstruction-based feature coding. Our method can be used as a universal one to improve the classification accuracies of many reconstruction-based coding methods, which is the highlight of our method. Concretely, a universal dictionary is learned by employing a reconstruction-based coding method and then refined for each category to obtain the category-specific dictionary of this category. When encoding a feature by a category-specific dictionary, the visual words for encoding it are decided in advance by the indices, which correspond to the non-zero elements of its coding vector obtained with the universal dictionary. The effectiveness of our method is validated by observing whether there is an accuracy improvement after applying our method. Our results on Scene-15, Caltech-101, and UIUC-Sports datasets show that the accuracies of four representative coding methods are improved by about 0.3% to 2.7%, which experimentally demonstrates the universality and effectiveness of our method.

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Analysis Dictionary Learning: an Efficient and Discriminative Solution

Cited by 12 publications

References 21 publications

Classification-Friendly Sparse Encoder and Classifier Learning

Classification-Friendly Sparse Encoder and Classifier Learning

Signal parameter estimation and classification using mixed supervised and unsupervised machine learning approaches

A Category-Specific Dictionary Learning Method Tailored for Reconstruction-Based Feature Coding

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