Sequential Dictionary Learning Research Articles

EEG-based schizophrenia classification using penalized sequential dictionary learning in the context of mobile healthcare

Mobile healthcare is an appealing approach based on the Internet of Medical Things (IoMT) and cloud computing. It can lead to unobstructed, economical, and patient-centric healthcare solutions. The key performance indicators of such systems are dimensionality reduction, computational effectiveness, low latency, and accuracy. In this context, a novel approach is devised for EEG-based schizophrenia, a severe mental disorder that adversely affects a person’s behavior and classification. A multichannel EEG recording with suitable granularity is required for precise analysis. It can increase exponentially the data dimensionality plus complexity and computational load. The proposed solution attains an interesting trade-off between dimensionality reduction plus computational effectiveness versus accuracy. It uses the penalized sequential dictionary learning (PSDL) that incorporates channel selection. First, PSDL learns a dictionary from the input data and evaluates its performance on all EEG channels. Based on this evaluation, a subset of six channels is selected for further training in the dictionary. The proposed PSDL algorithm then incorporates a penalty term that enhances the power of the learned dictionary on the selected channels. We evaluate the proposed approach on the multi-channel EEG dataset from the Institute of Psychiatry and Neurology in Warsaw, Poland. A performance comparison is also made with counterparts. The models’ performance depends on the EEG signals’ complexity. Therefore, we tried to make our models robust and straightforward, achieving appropriate performance with minimal computational cost. The proposed method reduces the dimension in two steps. First, the count of channels is reduced to 68.42%. In the second step, the kept information, 31.58% of channels, is further reduced to 83.75% using dictionary learning. The proposed framework secures a remarkable data dimension reduction and a lower computational cost and latency than the counterparts while attaining the sparse representation classification accuracy of 89.12%. These findings are promising and confirm the potential of investing in incorporating the proposed method in contemporary mobile healthcare solutions.

Hyperbolic function based hybrid consistent adaptive sequential DL algorithms for PIR sensor based intruder detection

This study demonstrates that a novel dictionary learning (DL) algorithm-based approach can be successfully used in conjunction with an indigenously developed hardware module, using four pyroelectric infrared (PIR) sensors, to enhance the performance of an intruder detection system. In this work, initially, a hyperbolic function based Consistent Adaptive Sequential Dictionary Learning (CAS-DL) algorithm has been proposed (HCAS-DL). Then another dictionary learning algorithm, called label consistency based K-SVD (LCK-SVD) is considered, where the label information of each dictionary atom has been introduced to enforce discriminability in sparse code. The objective function in LCK-SVD is formuated simultaneously considering the concept of label consistency (LC) constraints, reconstruction errors, and classification errors. Next, the newly proposed HCAS-DL is hybridized with LCK-SVD to form a novel version of hybrid consistent adaptive sequential dictionary learning approach, named here as the LC-HCAS-DL algorithm. Extensive experiments have been performed to establish the suitability of our proposed approach for the problem under consideration. The performance evaluation clearly establishes that the LC-HCAS-DL algorithm can achieve superior performance compared to recently proposed LC-MCAS-DL algorithm and other state-of-the-art algorithms for the intruder detection problem under consideration.

PIR-Sensor-Based Surveillance Tool for Intruder Detection in Secured Environment: A Label-Consistency-Based Modified Sequential Dictionary Learning Approach

In modern days, the importance of developing intelligent surveillance tools for activities of daily living is rising day by day. Identifying intruder(s) among the people who have access to a secured environment is crucial to achieve a flawless surveillance system in residential and office environments. This present work shows how a novel dictionary learning (DL) algorithm-based approach can be combined with four pyroelectric infrared (PIR) sensor-based indigenously developed hardware modules to design an intruder detection system. A novel DL approach is proposed combining the concepts of label consistency (LC) with a modified consistent adaptive sequential DL approach, named here as the LC-based modified consistent adaptive sequential DL (LC-MCAS-DL) algorithm. The conventional objective function in DL has been reformulated here by introducing the LC constraints along with reconstruction and classification errors. Then, the solution of this objective function is obtained by using a modified version of the consistent adaptive sequential DL algorithm (MCAS-DL). Extensive experiments have been performed to establish the suitability of our proposed approach for the problem under consideration.

An α -Divergence-Based Approach for Robust Dictionary Learning.

In this paper, a robust sequential dictionary learning (DL) algorithm is presented. The proposed algorithm is motivated from the maximum likelihood perspective on dictionary learning and its link to the minimization of the Kullback-Leibler divergence. It is obtained by using a robust loss function in the data fidelity term of the DL objective instead of the usual quadratic loss. The proposed robust loss function is derived from the α -divergence as an alternative to the Kullback-Leibler divergence, which leads to a quadratic loss. Compared to other robust approaches, the proposed loss has the advantage of belonging to class of redescending M-estimators, guaranteeing inference stability from large deviations from the Gaussian nominal noise model. The algorithm is obtained by solving a sequence of penalized rank-1 matrix approximation problems, where the l1 -norm is introduced as a penalty promoting sparsity and then using a block coordinate descent approach to estimate the unknowns. Performance comparison with similar robust DL algorithms on digit recognition, background removal, and gray-scale image denoising is performed highlighting efficacy of the proposed algorithm.

Consistent adaptive sequential dictionary learning

Abstract Algorithms for learning overcomplete dictionaries for sparse signal representation are mostly iterative minimization methods that alternate between a sparse coding stage and a dictionary update stage. For most however, the notion of consistency of the learned quantities has not been addressed. Based on the observation that the observed signals can be approximated as a sum of rank one matrices, a new adaptive dictionary learning algorithm is proposed in this paper. It is derived via sequential adaptive penalized rank one matrix approximation where the l1-norm is introduced as a penalty promoting sparsity. The proposed algorithm uses a block coordinate descent approach to consistently estimate the unknowns and has the advantage of having simple closed form solutions for both the sparse coding and dictionary update stages. The consistency properties of both the estimated sparse code and dictionary atom are provided. The performance of the proposed algorithm compared to some state of the art algorithms is illustrated on both simulated data and a real functional magnetic resonance imaging (fMRI) data set from a finger tapping experiment.

Urban vehicle detection in high-resolution aerial images via superpixel segmentation and correlation-based sequential dictionary learning

Vehicle detection in high-resolution aerial images has received widespread interests when it comes to providing the required information for traffic management and urban planning. It is challenging due to the relatively small size of the vehicles and the complex background. Furthermore, it is particularly challenging if the higher detection efficiency is required. Therefore, an urban vehicle detection algorithm is proposed via improved entropy rate clustering (IERC) and correlation-based sequential dictionary learning (CSDL). First, to enhance the detection accuracy, IERC is designed to generate more regular superpixels. It aims to avoid the situation that one superpixel sometimes straddles multiple vehicles. The generated superpixels are then treated as the seeds for the training sample selection. Then, CSDL is constructed to achieve a fast sequential training and updating of the dictionary. In CSDL, only the atoms correlated with the sparse representation of the new training data are inferred. Finally, comprehensive analyses and comparisons on two data sets demonstrate that the proposed method generates satisfactory and competitive results.

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods, such as independent component analysis for functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper, we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithms account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms is illustrated through simulations and applications on real fMRI data.