A Probabilistic Collaborative Representation Based Approach for Pattern Classification

Cai, Sijia; Zhang, Lei; Zuo, Wangmeng; Feng, Xiaoyi

doi:10.1109/cvpr.2016.322

Cited by 244 publications

(216 citation statements)

References 37 publications

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“…Cai et al [15] proposed the Probabilistic Collaborative Representation Based Classifier (ProCRC) algorithm for pattern classification. Let D = [ D 1 , D 2 ,…, D L ] ∈ ℝ M × N denote the training samples, where D l ∈ ℝ M × N l represents the training samples from the l th class with N l samples ( N = ∑ l =1 L N l ), and the dimension of each sample is M .…”

Section: Representation Based Classifiersmentioning

confidence: 99%

See 1 more Smart Citation

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Shu

Zhang

Tang

2017

Evidence-Based Complementary and Alternative Medicine

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At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

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Section: Representation Based Classifiersmentioning

confidence: 99%

“…In order to show the ProCRC procedure clearly, let D l ′ = [0,…, D l ,…, 0] ∈ ℝ M × N and

{\overset{D_{l}}{false}}^{'} = D - D_{l}^{'}

have the same size of D . More details about ProCRC can be found in [15]. …”

Section: Representation Based Classifiersmentioning

confidence: 99%

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Shu

Zhang

Tang

2017

Evidence-Based Complementary and Alternative Medicine

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show abstract

“…Local Contourlet Combined Patterns (LCCP) [8] reports a good performance in non-occlusion images but the recognition rate decreases in occlusion condition. There are some improvements [9], [10] for occlusion problem. The recent probabilistic collaborative representation (ProCRC) [10] jointly maximizes the likelihood of test samples with multiple classes.…”

Section: Introductionmentioning

confidence: 99%

“…There are some improvements [9], [10] for occlusion problem. The recent probabilistic collaborative representation (ProCRC) [10] jointly maximizes the likelihood of test samples with multiple classes.…”

Section: Introductionmentioning

confidence: 99%

Weighted Voting of Discriminative Regions for Face Recognition

Yang

Gao

Liao

2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.

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“…The proposed LGECSDL algorithm is compared with another seven classical face recognition algorithms: nearest neighbor (NN) classification, collaborative representation based classification (CRC) [30], sparse representation based classification (SRC) [31], kernel-based probabilistic collaborative representation based classifier (ProKCRC) [32], VGG19 [33], kernel-based class specific dictionary learning (KCSDL) algorithm [29], and SVM [34].…”

Section: Experimental Settingsmentioning

confidence: 99%

Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition

Wang

Liu

2018

Journal of Electrical and Computer Engineering

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The sparse representation based classification (SRC) method and collaborative representation based classification (CRC) method have attracted more and more attention in recent years due to their promising results and robustness. However, both SRC and CRC algorithms directly use the training samples as the dictionary, which leads to a large fitting error. In this paper, we propose the Laplace graph embedding class specific dictionary learning (LGECSDL) algorithm, which trains a weight matrix and embeds a Laplace graph to reconstruct the dictionary. Firstly, it can increase the dimension of the dictionary matrix, which can be used to classify the small sample database. Secondly, it gives different dictionary atoms with different weights to improve classification accuracy. Additionally, in each class dictionary training process, the LGECSDL algorithm introduces the Laplace graph embedding method to the objective function in order to keep the local structure of each class, and the proposed method is capable of improving the performance of face recognition according to the class specific dictionary learning and Laplace graph embedding regularizer. Moreover, we also extend the proposed method to an arbitrary kernel space. Extensive experimental results on several face recognition benchmark databases demonstrate the superior performance of our proposed algorithm.

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A Probabilistic Collaborative Representation Based Approach for Pattern Classification

Cited by 244 publications

References 37 publications

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Weighted Voting of Discriminative Regions for Face Recognition

Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition

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