A Novel Two-Dimensional Tensor Sparse Coding Algorithm for Image Representation

Sparse coding-based transfer learning for energy disaggregation

With the rapid development of various satellite sensors, automatic and advanced scene classification technique is urgently needed to process a huge amount of satellite image data. Recently, a few of research works start to implant the sparse coding for feature learning in aerial scene classification. However, these previous research works use the single-layer sparse coding in their system and their performances are highly related with multiple low-level features, such as scale-invariant feature transform (SIFT) and saliency. Motivated by the importance of feature learning through multiple layers, we propose a new unsupervised feature learning approach for scene classification on very high resolution satellite imagery. The proposed unsupervised feature learning utilizes multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex satellite scene images. In addition, the dense low-level features are extracted from the raw satellite data by using different image patches with varying size at different layers, and this approach is not limited to a particularly designed feature descriptors compared with the other related works. The proposed technique has been evaluated on two challenging high-resolution datasets, including the UC Merced dataset containing 21 different aerial scene categories with a 1 foot resolution and the Singapore dataset containing 5 land-use categories with a 0.5m spatial resolution. Experimental results show that it outperforms the state-of-the-art that uses the single-layer sparse coding. The major contributions of this proposed technique include (1) a new unsupervised feature learning approach to generate feature representation for very high-resolution satellite imagery, (2) the first multipath sparse coding that is used for scene classification in very high-resolution satellite imagery, (3) a simple low-level feature descriptor instead of many particularly designed low-level descriptor, such as SIFT descriptors and saliency, (4) evaluation on two satellite image datasets that come from different sensor sources.

Non-negative and local sparse coding based on l2-norm and Hessian regularization

Impulsive wavelet based probability sparse coding model for bearing fault diagnosis

In this paper, we propose the convolutional sparse coding based model in the wavelet domain for the task of single image super-resolution (SISR). The conventional sparse coding based approaches work on overlapping image patches and use the dictionary atoms to sparse code an image patch. Further, at the final stage, an overlap-add mechanism is used to get the final high-resolution image estimate. However, these algorithms fail to take into account the consistency present in the overlapping patches which limits their performance. We propose the use of wavelet integrated convolutional sparse coding approach where instead of dictionary atoms we utilize the convolution summations between the learned filters and their mappings for sparse representation based SISR. The use of wavelets is proposed owing to their unique directional and compact features. A pair of filters are learned along with a mapping function for each wavelet sub-band to exploit the consistency among patches. The proposed wavelet integrated convolutional sparse coding model helps capture useful contextual information. The proposed model is evaluated on publicly available datasets for different scale-up parameters. To show the efficacy of the proposed model we compare it with recent state-of-the-art algorithms. The visual results along with the quantitative ones indicate that the proposed model performs well for the tasks of super-resolution.

This chapter concentrates the problem of recovery a high-resolution (HR) image from a single low-resolution input image. Recent research proposed to deal with the image super-resolution problem with sparse coding, which is based on the well reconstruction of any local image patch by a sparse linear combination of an appropriately chosen over-complete dictionary. Therein the chosen LR (Low-resolution) and HR (High-resolution) dictionaries have to be exactly corresponding for well reconstructing the local image patterns. However, the conventional sparse coding based image super-resolution usually achieves a global dictionary D=[D l ; D h ] by jointly training the concatenated LR and HR local image patches, and then reconstruct the LR and HR image as a linear combination of the separated D l and D h . This strategy only can achieve the global minimum reconstructing error of LR and HR local patches, and usually cannot obtain the exactly corresponding LR and HR dictionaries. In addition, the accurate coefficients for reconstructing the HR image patch using HR dictionary D h are also unable to be estimated using only the LR input and the LR dictionary D l . Therefore, this paper proposes to firstly learn the HR dictionary D h from the features of the training HR local patches, and then propagates the HR dictionary to the LR one, called as HR2LR dictionary propagation, by mathematical proving and statistical analysis. The effectiveness of the proposed HR2LR dictionary propagation in sparse coding for super-resolution is demonstrated by comparison with the conventional super-resolution approaches such as sparse coding and interpolation.

Sparse coding with morphology segmentation and multi-label fusion for hyperspectral image super-resolution

In this paper, a new spatial neighborhood-constrained linear coding strategy which realizes sparse representation is proposed for visual object tracking. Unlike conventional sparse and locality-constrained linear coding approaches that need an extra post-processing stage to incorporate the spatial layout information, the proposed coding strategy intrinsically embeds the spatial layout information into the coding stage. The proposed coding strategy can also be used to effectively realize joint sparse representation for different feature descriptors. In addition, based on the distance to the “ideal point” in the reconstruction error space, a new multicue integration approach for robust tracking is proposed and a co-learning approach is developed to update the dictionaries. Finally, the proposed tracking algorithm is compared with other state-of-the-art trackers on some challenging video sequences and shows promising results.

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, Information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques.

Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of features to use for a given task or data set. Therefore, there is a need to develop automatic feature selection techniques. In this paper we describe a greedy procedure for automatically selecting features to use within the Markov random field model for information retrieval. We also propose a novel, robust method for describing classes of textual information retrieval features. Experimental results, evaluated on standard TREC test collections, show that our feature selection algorithm produces models that are either significantly more effective than, or equally effective as, models with manually selected features, such as those used in the past.

Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Introduction: The early and accurate detection of COVID-19 remains a life-threatening challenge in medical analysis. Machine learning is used for predicting disease outcomes based on clinical parameters. This analysis proposes a comparative analysis of feature selection method and classification techniques to enhance COVID-19 detection accuracy using blood biomarkers. We used a pensourse dataset of 1,724 cases, including 35 features. To improve the model performance data preprocessing process included outlier handling, normalization, and transformation techniques to improve model performance. To identify the relevant features, we employed the three-feature selection methods Chi-Square test, Pearson correlation coefficient, and Random Forest. The model prediction accuracy was enhanced using a stacking ensemble classication techniques. The machine learning based classification models effectively predicted COVID-19 infectious disease using blood biomarkers with optimized feature selection techniques. Objectives: To enhance the accuracy of COVID-19 prediction using machine learning techniques by applying feature selection and classification techniques on blood biomarkers. Methods: The comparative analysis utilized a publicly available dataset containing 1,724 cases with 35 attributes. Data preprocessing involved outlier handling, normalization, and transformation techniques. Employed Chi-Square test, Pearson correlation coefficient, and Random Forest feature selection techniques. Stacking ensemble classification algorithm was utilized for the better performance of a model. Results: The classification models demonstrated efficiency in predicting COVID-19 using blood biomarkers. Optimized feature selection significantly improved predictive accuracy, highlighting the importance of selecting relevant features for model performance enhancement. Conclusions: This study showcases the potential of ML-driven approaches for COVID-19 detection, emphasizing the role of feature selection in improving classification accuracy. The findings contribute to the advancement of diagnostic tools, offering a data-driven solution for rapid and reliable COVID-19 screening.

Exploration of novel computational platforms is critical for the advancement of artificial intelligence as we approach the physical limitations of traditional hardware. Biologically accurate, energy efficient neuromorphic systems are particularly promising for enabling future breakthroughs because of their ability to process information in parallel and to scale using extremely low power. Sparse coding is a signal processing technique which has been known to model the information encoding in the primary visual cortex. When sparse solutions are solved using local neuron competition along with the unsupervised dictionary learning that mimics cortical development, we can build an end to end, hardware to software, brain inspired solution to a machine learning problem. In this article, we perform a detailed comparison of sparse coding solutions generated classically by orthogonal matching pursuit (OMP) implemented on a conventional digital processor with spike-based solutions obtained using the Intel Loihi neuromorphic processor. A novel “post-hoc” normalization technique to shorten simulation time for Loihi is presented along with analysis of optimal parameter selection, reconstruction errors, and unsupervised dictionary learning for Loihi approaches and their classical counterparts. Preliminary results show that both the Loihi full simulation approach and the post-hoc normalization approach are well suited to neuromorphic processors and operate in a size, weight and power regime that is not accessible by classical approaches. Ultimately, the use of this normalization technique allows for faster and, often, better solutions than demonstrated previously.

Text clustering involves data that are of very high dimension. Feature selection techniques find subsets of relevant features from the original feature space that help in efficient and effective clustering. Selection of relevant features merely on ranking scores without considering correlation interferes with the clustering performance. An efficient feature selection technique should be capable of preserving the multi-cluster structure of the data. The purpose of the present work is to demonstrate that feature selection techniques which take into consideration the correlation among features in multi-cluster scenario show better clustering results than those techniques that simply rank features independent of each other. This paper compares two feature selection techniques in this regard viz. the traditional Tf-Idf and the Multi-Cluster Feature Selection (MCFS) technique. The experimental results over the TDT2 and Reuters-21,578 datasets show the superior clustering results of MCFS over traditional Tf-Idf.

Fisher discrimination dictionary sparse learning (FDDL) has led to interesting image recognition results where the Fisher discrimination criterion is subject to the coding coefficients. But Fisher discrimination criterion has the limitations of data distribution assumptions and does not consider the local manifold structure of the coding coefficients. In this paper, we will introduce a novel Fisher discrimination sparse learning based on graph embedding (GE-FDSL) scheme. First, we utilizes graph embedding framework to define intra-class compact matrix and inter-class separable matrix imposed on the coding coefficients of training samples to preserving the intra-class compactness and the inter-class separability for the training samples, which simultaneously consider the local manifold structure and label information of the coding coefficients. Then, a new Fisher discrimination criterion based on graph embedding is added to the object function of the sparse coding problem so that the coding coefficients have more discriminative power, where the dictionary atoms in the sparse coding model are associated with the class labels so that the reconstructed error is applied to classification. This method can learn a structured dictionary and sparse coefficients, and in the meantime, it will also keep the local manifold structure of the coding coefficients. So, they will be more discriminative. Experiments on many image databases show that the our algorithm has good classification and recognition performance.