An elastic competitive and discriminative collaborative representation method for image classification.

Collaborative representation-based (CR) methods have become prevalent for pattern classification tasks, achieving formidable performance. Theoretically, we expect the learned class-specific representation of the correct class to be discriminative against others, with the representation of the correct class contributing dominantly in CR. However, most existing CR methods focus on improving discrimination while having a limited impact on enhancing the representation contribution of the correct category. In this work, we propose a novel CR approach for image classification called the elastic competitive and discriminative collaborative representation-based classifier (ECDCRC) to simultaneously strengthen representation contribution and discrimination of the correct class. The ECDCRC objective function penalizes two key terms by fully incorporating label information. The competitive term integrates the nearest subspace representation with corresponding elastic factors into the model, allowing each class to have varying competition intensities based on similarity with the query sample. This enhances the representation contribution of the correct class in CR. To further improve discrimination, the discriminative term introduces an elastic factor as a weight in the model to represent the gap between the query sample and the representation of each class. Moreover, instead of focusing on representation coefficients, the designed ECDCRC weights associated with representation components directly relate to the representation of each class, enabling more direct and precise discrimination improvement. Concurrently, sparsity is also enhanced through the two terms, further boosting model performance. Additionally, we propose a robust ECDCRC (R-ECDCRC) to handle image classification with noise. Extensive experiments on seven public databases demonstrate the proposed method's superior performance over related state-of-the-art CR methods.

Hierarchical neural network with efficient selection inference.

The image classification precision is vastly enhanced with the growing complexity of convolutional neural network (CNN) structures. However, the uneven visual separability between categories leads to various difficulties in classification. The hierarchical structure of categories can be leveraged to deal with it, but a few CNNs pay attention to the character of data. Besides, a network model with a hierarchical structure is promising to extract more specific features from the data than current CNNs, since, for the latter, all categories have the same fixed number of layers for feed-forward computation. In this paper, we propose to use category hierarchies to integrate ResNet-style modules to form a hierarchical network model in a top-down manner. To extract abundant discriminative features and improve the computation efficiency, we adopt residual block selection based on coarse categories to allocate different computation paths. Each residual block works as a switch to determine the JUMP or JOIN mode for an individual coarse category. Interestingly, since some categories need less feed-forward computation than others by jumping layers, the average inference time cost is reduced. Extensive experiments show that our hierarchical network achieves higher prediction accuracy with similar FLOPs on CIFAR-10 and CIFAR-100, SVHM, and Tiny-ImageNet datasets compared to original residual networks and other existing selection inference methods.

Principal Component Analysis based on Nuclear norm Minimization.

Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent years, massive efforts have been made to improve the robustness of PCA. However, many emerging PCA variants developed in the direction have some weaknesses. First, few of them pay attention to the 2D structure of error matrix. Second, to estimate data mean from sample set with outliers by averaging is usually biased. Third, if some elements of a sample are disturbed, to extract principal components (PCs) by directly projecting data with transformation matrix causes incorrect mapping of sample to its genuine location in low-dimensional feature subspace. To alleviate these problems, we present a novel robust method, called nuclear norm-based on PCA (N-PCA) to take full advantage of the structure information of error image. Meanwhile, it is developed under a novel unified framework of PCA to remedy the bias of computing data mean and the low-dimensional representation of a sample both of which are treated as unknown variables in a single model together with projection matrix. To solve N-PCA, we propose an iterative algorithm, which has a closed-form solution in each iteration. Experimental results on several open databases demonstrate the effectiveness of the proposed method.

Abnormal amplitude of low-frequency fluctuations associated with rapid-eye movement in chronic primary insomnia patients.

PURPOSE: Chronic primary insomnia (CPI) is the most prevalent sleep disorder worldwide. CPI manifests as difficulties in sleep onset, maintaining sleep, prolonged sleep latency, and daytime impairment and is often accompanied by cognitive problems such as poor academic performance, poor attention, and decreased memory. The most popular explanation of insomnia is hyperarousal or increased activities of neurons. Rapid eye movement (REM) sleep detected by polysomnography (PSG) exhibits a positive relationship with brain homeostasis and can be helpful for optimally preparing an organism for emotional and social function. Limited work has been performed to explore brain function of insomnia patients in combination with PSG analysis. RESULTS: We observed increased ALFF within areas related to hyperarousal such as the midbrain and bilateral extra-nucleus, whereas decreased ALFF was observed within areas associated with memory and attention involving the parietal and occipital lobule and others. Furthermore, the altered ALFF was associated with the duration of insomnia, sleep efficiency, duration of REM, latency of RME and ratio of REM. MATERIALS AND METHODS: In this study, we recruited twenty-five CPI patients and twenty-five normal sleep (NS) volunteers as a control group to investigate the amplitude of low-frequency fluctuations (ALFF) and the correlation between those altered ALFF regions through resting-state fMRI and PSG data. CONCLUSIONS: These findings suggest that hyperarousal reflected by ALFF abnormality within brain areas related to cognition and emotion in insomnia associated with REM sleep.

Comparison of Gating Properties and Use-Dependent Block of Nav1.5 and Nav1.7 Channels by Anti-Arrhythmics Mexiletine and Lidocaine.

Mexiletine and lidocaine are widely used class IB anti-arrhythmic drugs that are considered to act by blocking voltage-gated open sodium currents for treatment of ventricular arrhythmias and relief of pain. To gain mechanistic insights into action of anti-arrhythmics, we characterized biophysical properties of Nav1.5 and Nav1.7 channels stably expressed in HEK293 cells and compared their use-dependent block in response to mexiletine and lidocaine using whole-cell patch clamp recordings. While the voltage-dependent activation of Nav1.5 or Nav1.7 was not affected by mexiletine and lidocaine, the steady-state fast and slow inactivation of Nav1.5 and Nav1.7 were significantly shifted to hyperpolarized direction by either mexiletine or lidocaine in dose-dependent manner. Both mexiletine and lidocaine enhanced the slow component of closed-state inactivation, with mexiletine exerting stronger inhibition on either Nav1.5 or Nav1.7. The recovery from inactivation of Nav1.5 or Nav1.7 was significantly prolonged by mexiletine compared to lidocaine. Furthermore, mexiletine displayed a pronounced and prominent use-dependent inhibition of Nav1.5 than lidocaine, but not Nav1.7 channels. Taken together, our findings demonstrate differential responses to blockade by mexiletine and lidocaine that preferentially affect the gating of Nav1.5, as compared to Nav1.7; and mexiletine exhibits stronger use-dependent block of Nav1.5. The differential gating properties of Nav1.5 and Nav1.7 in response to mexiletine and lidocaine may help explain the drug effectiveness and advance in new designs of safe and specific sodium channel blockers for treatment of cardiac arrhythmia or pain.

A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly, a new data matrix is constructed by decomposing the total scatter matrix. Thirdly, the new data matrix is decomposed by PMD to obtain the sparse eigensamples. Finally, the core genes are identified according to the nonzero entries in eigensamples. The results on simulation data show that CIPMD method can reach higher identification accuracies than the conventional gene identification methods. Moreover, the results on real gene expression data demonstrate that CIPMD method can identify more core genes closely related to the abiotic stresses than the other methods.

A novel algorithm for independent component analysis with reference and methods for its applications.

This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.

Robust PCA based method for discovering differentially expressed genes.

How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.

Face recognition using sparse representation-based classification on k-nearest subspace.

The sparse representation-based classification (SRC) has been proven to be a robust face recognition method. However, its computational complexity is very high due to solving a complex l(1)-minimization problem. To improve the calculation efficiency, we propose a novel face recognition method, called sparse representation-based classification on k-nearest subspace (SRC-KNS). Our method first exploits the distance between the test image and the subspace of each individual class to determine the k nearest subspaces and then performs SRC on the k selected classes. Actually, SRC-KNS is able to reduce the scale of the sparse representation problem greatly and the computation to determine the k nearest subspaces is quite simple. Therefore, SRC-KNS has a much lower computational complexity than the original SRC. In order to well recognize the occluded face images, we propose the modular SRC-KNS. For this modular method, face images are partitioned into a number of blocks first and then we propose an indicator to remove the contaminated blocks and choose the k nearest subspaces. Finally, SRC is used to classify the occluded test sample in the new feature space. Compared to the approach used in the original SRC work, our modular SRC-KNS can greatly reduce the computational load. A number of face recognition experiments show that our methods have five times speed-up at least compared to the original SRC, while achieving comparable or even better recognition rates.

New robust face recognition methods based on linear regression.

Nearest subspace (NS) classification based on linear regression technique is a very straightforward and efficient method for face recognition. A recently developed NS method, namely the linear regression-based classification (LRC), uses downsampled face images as features to perform face recognition. The basic assumption behind this kind method is that samples from a certain class lie on their own class-specific subspace. Since there are only few training samples for each individual class, which will cause the small sample size (SSS) problem, this problem gives rise to misclassification of previous NS methods. In this paper, we propose two novel LRC methods using the idea that every class-specific subspace has its unique basis vectors. Thus, we consider that each class-specific subspace is spanned by two kinds of basis vectors which are the common basis vectors shared by many classes and the class-specific basis vectors owned by one class only. Based on this concept, two classification methods, namely robust LRC 1 and 2 (RLRC 1 and 2), are given to achieve more robust face recognition. Unlike some previous methods which need to extract class-specific basis vectors, the proposed methods are developed merely based on the existence of the class-specific basis vectors but without actually calculating them. Experiments on three well known face databases demonstrate very good performance of the new methods compared with other state-of-the-art methods.

A new constrained independent component analysis method.

Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved algorithm for cICA is presented through the investigation of the inequality constraints, in which different closeness measurements are compared. The utility of our proposed algorithm is demonstrated by the experiments with synthetic data and electroencephalogram (EEG) data.