Weighted Sparse Representation-based Classification Research Articles

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information.

Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods.

Multivariate copula statistical model and weighted sparse classification for radar image target recognition

We propose in this paper a new method for targets recognition from radar images. To characterize the radar images, we adopt a statistical multivariate modeling using copula in the complex wavelet domain. For the recognition step, we investigate the weighted sparse representation-based classification (WSRC) method. To build the dictionary, the estimated copula parameters are stacked together in a matrix structure. In order to include the locality information of this dictionary for each unknown radar image to recognize, we affect weights for its atoms (columns). That is done by calculating the Kullback–Leibler divergence (KLD) between the multivariate copula parameters of training and test radar images. Finally, the unknown radar image is recognized through the SRC classifier. Several empirical results carried out on the SAR (synthetic aperture radar) and ISAR (inverse synthetic aperture radar) images demonstrate that the proposed method achieves high recognition rates and outperforms the remaining methods.

FCTP-WSRC: Protein-Protein Interactions Prediction via Weighted Sparse Representation Based Classification.

The task of predicting protein–protein interactions (PPIs) has been essential in the context of understanding biological processes. This paper proposes a novel computational model namely FCTP-WSRC to predict PPIs effectively. Initially, combinations of the F-vector, composition (C) and transition (T) are used to map each protein sequence onto numeric feature vectors. Afterwards, an effective feature extraction method PCA (principal component analysis) is employed to reconstruct the most discriminative feature subspaces, which is subsequently used as input in weighted sparse representation based classification (WSRC) for prediction. The FCTP-WSRC model achieves accuracies of 96.67%, 99.82%, and 98.09% for H. pylori, Human and Yeast datasets respectively. Furthermore, the FCTP-WSRC model performs well when predicting three significant PPIs networks: the single-core network (CD9), the multiple-core network (Ras-Raf-Mek-Erk-Elk-Srf pathway), and the cross-connection network (Wnt-related Network). Consequently, the promising results show that the proposed method can be a powerful tool for PPIs prediction with excellent performance and less time.

Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in need for a large number of real-time applications such as hands and touch-free text entry system, movement of a wheelchair, movement of a cursor, prosthetic arm movement, virtual reality systems, etc. In recent years, sparse representation-based classification (SRC) is a growing technique and has been a successful technique on classifying MI-based Electroencephalography (EEG) signals. To further boost the proficiency of SRC technique, in this paper, a weighted SRC (WSRC) has been proposed for classifying MI signals. In WSRC approach, a weighted dictionary has been constructed according to the dissimilarity information between a test data and training samples. Then for the given test data, the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives discriminative information and as a consequence, WSRC proves to be superior for MI-based EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.

Face Recognition Using the Combination of Weighted Sparse Representation-based Classification and Singular Value Decomposition Face

Given the increasing need for the creation and development of automated systems, the problem of detecting and identifying the faces of people in the images has been considered by the researchers. In recent years, the sparse representation based classification has been of great interest to researchers. The goal of this investigation is to provide a quick and effective way to identify faces based on the sparse representation. Since the basis of sparse representation is to calculate it through L1-norm optimization for high dimensional dictionary with high computational volume, a smoothed L0-norm optimization-based method was introduced. At the time of obtaining the sparse representation using smoothed L0-norm, due to the high degree of redundancy, when the number of facial features is low, the conditions for the unique sparse representation are not properly satisfied or the model is not accurate, the risk of getting stuck in the local minima will be so much. Hence, the idea of the weighted sparse representation to narrow the search space is presented. In addition, to enhance the identification accuracy in face recognition and face verification under varying illumination conditions, the Singular Value Decomposition method can be used in the feature extraction step. In the extraction stage, the normalized coefficients of Singular Value Decomposition are not sensitive to different lightness conditions, resulting to reduce the effect of illumination variation on the normalized images. Compared to earlier methods, which often fail even in the event of a slight turbulence, this technique can successfully produce facial structural information while preventing the effect of different lightnesses such as cast shadows. The simulation results on the Extended Yale B database show that the proposed method has high accuracy in face recognition with less computational volume and higher speed than the L1-norm based approach.

Palmprint identification combining hierarchical multi-scale complete LBP and weighted SRC

Palmprint is one of the most reliable biometrics and has been widely used for human identification due to its high recognition accuracy and convenience for practical application. But the existing palmprint-based human identification system often suffers from image misalignment, pixel corruption and much computational time on the large database. An effective palmprint recognition method is proposed by combining hierarchical multi-scale complete local binary pattern (HMS-CLBP) and weighted sparse representation-based classification (WSRC). The hierarchical multi-scale local invariant texture features are extracted firstly from each palmprint by multi-scale local binary pattern (MS-LBP) and multi-scale complete local binary pattern (MS-CLBP) and are concatenated into one hierarchical multi-scale fusion feature vector. Then, WSRC is constructed by the Gaussian kernel distance, and use the Gaussian kernel distances between the fusion feature vectors of the training and testing samples. Finally, the sparse decomposition of testing samples is implemented by solving the optimization problem based on l1 norm, and the palmprints are recognized by the minimum residuals. The proposed method inherits the advantages of CLBP and WSRC and has good rotation, illumination and occlusion invariance. The results on the PolyU and CASIA palmprint databases illustrate the good performance and rationale interpretation of the proposed method.

Weighted sparse representation for classification of motor imagery EEG signals.

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in demand for many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality, movement of wheelchairs, etc. Traditional sparse representation based classification (SRC) is a thriving technique in recent years and has been a successful approach for classifying MI EEG signals. To further improve the capability of SRC, in this paper, a weighted SRC (WSRC) has been proposed for classifying two-class MI tasks (right-hand, right-foot). WSRC constructs a weighted dictionary according to the dissimilarity information between the test data and the training samples. Then for the given test data the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives better discriminative information than SRC and as a consequence, WSRC proves to be superior for MI EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.

Collaborative representation with k-nearest classes for classification

Sparse representation based classification (SRC) and collaborative representation based classification (CRC), have been widely used in many fields such as face recognition and hyperspectral image classification. All known pattern classes of the training samples are used by SRC and CRC to collaborative represent a test sample, which may be time-consuming and unnecessary. This paper proposes the use of only a part of known pattern classes to collaboratively represent a test sample. Specifically, only the first k (a pre-given constant) classes that are nearest to the sample to be tested are used. With these k-nearest classes (KNCs), SRC and CRC, each can be generalized as KNCs based SRC (KNCSRC) and KNCs based CRC(KNCCRC), and conversely, SRC and CRC can be seen as special cases of KNCSRC and KNCCRC, respectively. Weighted SRC (WSRC) and weighted CRC (WCRC) also can be generalized as KNCs based WSRC (KNCWSRC) and KNCs based WCRC (KNCWSRC), respectively. Competitive classification experiments are carried out on five databases to evaluate the proposed classification scheme. The experimental results show that, in all cases, the proposed KNCSRC and KNCCRC outperform SRC and CRC, respectively.The results also prove that the proposed KNCWCRC outperforms WCRC. In conclusion, collaborative representation with KNCs can further improve collaborative representation based classification.

Combining Modified LBP and Weighted SRC for Palmprint Recognition

In order to extract invariant features in the palmprint transformation of scale, rotation and affine distortion, a coarse-to-fine palmprint recognition method is proposed by combining the weighted adaptive center symmetric local binary pattern (WACS-LBP) and weighted sparse representation based classification (WSRC). The method consists of coarse and fine stages. In the coarse stage, using the similarity between the test sample and one sample of each training class, most of the training classes could be excluded and a small number of candidate classes of the test sample are reserved. Thus, the original classification problem becomes clear and simple. In the fine stage, the robust rotation invariant weighted histogram feature vector is extracted from each candidate sample and the test sample by WACS-LBP, and the weighted sparse representation optimal problem is constructed by the similarity between the test sample and each candidate training sample, and the test sample is recognized by the minimum residual. The proposed method is tested and compared with the existing algorithms on the PolyU and CASIA database. The experimental results illustrate better performance and rationale interpretation of the proposed method.

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition.

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed.

Discriminant WSRC for Large-Scale Plant Species Recognition.

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.

With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein–protein interactions (PPIs) research is becoming more and more important. Life activities and the protein–protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction.

Face recognition via Weighted Sparse Representation

Face recognition using Sparse Representation based Classification (SRC) is a new hot technique in recent years. SRC can be regarded as a generalization of Nearest Neighbor and Nearest Feature Subspace. This paper first reviews the Nearest Feature Classifiers (NFCs), including Nearest Neighbor (NN), Nearest Feature Line (NFL), Nearest Feature Plane (NFP) and Nearest Feature Subspace (NFS), and formulates them as general optimization problems, which provides a new perspective for understanding NFCs and SRC. Then a locality Weighted Sparse Representation based Classification (WSRC) method is proposed. WSRC utilizes both data locality and linearity; it can be regarded as extensions of SRC, but the coding is local. Experimental results on the Extended Yale B, AR databases and several data sets from the UCI repository show that WSRC is more effective than SRC.