Sparse Classification Approach Research Articles

Enhanced dictionary learning based sparse classification approach with applications to planetary bearing fault diagnosis

To date, planetary bearings remain challenging for machinery fault diagnosis because of their intricate kinematics, time-variant modulations, and strong interferences. To address this challenge, this study presents an enhanced dictionary learning based sparse classification (EDL-SC) approach to diagnose planetary bearings. Our main novelty lies in that the data augmentation and dictionary learning strategies are incorporated into our proposed EDL-SC approach, which can significantly enhance the representation ability and recognition ability for the sparse classification-based intelligent diagnosis criterion. Firstly, vibration data augmentation is implemented with an overlapping segmentation strategy to enhance the quality of training samples. Secondly, data-driven dictionary design is achieved by means of dictionary learning, which learns sub-dictionaries and adaptively designs the whole dictionary through considering both the inter-class and intra-class features. Thirdly, a sparse classification strategy is established for intelligent diagnostics by the aid of a discrimination criterion of minimal reconstruction errors. The feasibility and advantage of EDL-SC have been thoroughly evaluated with a challenging planetary bearing dataset. Experiment verification results of planetary bearing fault diagnosis indicate that EDL-SC obtains a superior diagnosis accuracy of 99.63%, strong robustness to noises, and competitive computation efficiency over advanced deep learning and sparse representation classification methods. This work can bring new insights for the application of sparse representation theory from the perspective of pattern recognition, and shows great potentials of EDL-SC for data-driven machinery fault diagnosis.

Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application

This paper depicts a novel semi-supervised classification model with convolutional neural networks (CNN) for EEG Recognition. The performance of popular machine learning algorithm usually rely on the number of labeled training samples, such as the deep learning approaches,sparse classification approaches and supervised learning approaches. However, the labeled samples are very difficulty to get for electroencephalography(EEG) data. In addition, most deep learning algorithms are usually time-consuming in the process of training. Considering these problems, in this article, a novel semi-supervised quantization algorithm based on the cartesian K-means algorithm is proposed, which named it as the semi-supervised cartesian K-means (SSCK), we use the CNN models pre-trained on motor imagery samples to create deep features, and then we applied it for motor imagery (MI) data classification. Unlike the traditional semi-supervised learning models that labeled information can be directly casted into the model training, label information can only be implicitly used in the semi-supervised learning strategy, in the semi-supervised learning algorithm, supervised information is integrated into the quantization algorithm by resorting a supervised constructed laplacian regularizer. Experimental results over four popular EEG datasets substantiate the efficiency and effectiveness of our proposed semi-supervised cartesian K-means.

Differential privacy for sparse classification learning

In this paper, we present a differential privacy version of convex and nonconvex sparse classification approach. Based on alternating direction method of multiplier (ADMM) algorithm, we transform the solving of sparse problem into the multistep iteration process. Then we add exponential noise to stable steps to achieve privacy protection. By the property of the post-processing holding of differential privacy, the proposed approach satisfies the ϵ−differential privacy even when the original problem is unstable. Furthermore, we present the theoretical privacy bound of the differential privacy classification algorithm. Specifically, the privacy bound of our algorithm is controlled by the algorithm iteration number, the privacy parameter, the parameter of loss function, ADMM pre-selected parameter, and the data size. Finally we apply our framework to logistic regression with L1 regularizer and logistic regression with L1/2 regularizer. Numerical studies demonstrate that our method is both effective and efficient which performs well in sensitive data analysis.

Sparse classification using Group Matching Pursuit

Group structure exists in supervised learning problems inherently. For example, in the training data of a classification problem, samples from the same class will have similar representations. These samples from the same class will form a group. Group sparse features, such as Group Lasso, can reserve this group information and improve classification accuracy by reducing noise using sparse constraints. However, Group Lasso is a sparse approximation, and its performance and solution highly depend on the choice of group sparse penalty weight. In this paper, we propose a Group Matching Pursuit (GMP) based sparse classification approach. Group Matching Pursuit features are sparse features where coefficients of exact K groups are non-zeros. Compared to Group Lasso, 1. Group Matching Pursuit based classification is stable and has no penalty parameters to tune; 2. Group Matching Pursuit features have large Signal-to-Noise Ratio (SNR) and are class discriminant; 3. Group Matching Pursuit features are also robust for occluded images. Extensive experiments on 5 data sets and different levels of occlusion show that the proposed Group Matching Pursuit can improve Group Lasso classification accuracy up to 27.2% in some situation.

Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification framework. The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero. In particular, two different sparse classification approaches, i.e. sPCA+kNN and sPLS-DA, were compared with the corresponding classical methods (PCA+kNN and PLS-DA) to classify Arabica and Robusta coffee species. Green coffee samples were analyzed using near infrared hyperspectral imaging and the average spectra from each hyperspectral image were used to build training and test sets; furthermore a test image was used to evaluate the performances of the considered methods at pixel-level. In our case, sparse methods led to similar results as classical methods, with the advantage of obtaining more interpretable and parsimonious models. An important result to highlight is that variable selection performed with two different sparse classification approaches converged to the selection of same spectral regions, which implies the chemical relevance of those regions in the discrimination of Arabica and Robusta coffee species.

Robust radar waveform recognition algorithm based on random projections and sparse classification

To solve the limitations existing in the feature-based recognition method in information completeness and redundant of the representation and the noise robustness, a radar waveform recognition algorithm based on random projections and sparse classification (SC) is presented. Construction of the framework of the proposed algorithm consists of two phases. In the first phase, the compressed signals by random projections are used instead of traditional signal features to represent the original signal. In the second, the robust SC approach is investigated to the radar waveform recognition. Compared with the existing feature-based recognition method, the random projections and sparse classification-based algorithm can improve the information completeness, efficiency and the noise robust. The validity of the recognition algorithm is demonstrated with the analysis and simulations.

A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples

The classification of high-dimensional data with too few labeled samples is a major challenge which is difficult to meet unless some special characteristics of the data can be exploited. In remote sensing, the problem is particularly serious because of the difficulty and cost factors involved in assignment of labels to high-dimensional samples. In this paper, we exploit certain special properties of hyperspectral data and propose an l1-minimization -based sparse representation classification approach to overcome this difficulty in hyperspectral data classification. We assume that the data within each hyperspectral data class lies in a very low-dimensional subspace. Unlike traditional supervised methods, the proposed method does not have separate training and testing phases and, therefore, does not need a training procedure for model creation. Further, to prove the sparsity of hyperspectral data and handle the computational intensiveness and time demand of general-purpose linear programming (LP) solvers, we propose a Homotopy-based sparse classification approach, which works efficiently when data is highly sparse. The approach is not only time efficient, but it also produces results, which are comparable to the traditional methods. The proposed approaches are tested for our difficult classification problem of hyperspectral data with few labeled samples. Extensive experiments on four real hyperspectral data sets prove that hyperspectral data is highly sparse in nature, and the proposed approaches are robust across different databases, offer more classification accuracy, and are more efficient than state-of-the-art methods.