Traditional Methods Research Articles

A Study of Hierarchical Risk Parity in Portfolio Construction

<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.</span></p>

Hydrogenation Influences the Created Giant Dielectric Behaviors of (Bi+W) Codoped Anatase TiO2

Introductions: TiO2 nanoparticles ceramic (NPs) codoped with Bi and W ions have been synthesized by a hydrothermal technique. A portion of the prepared ceramic was posthydrogenated. Ceramic NPs were characterized by traditional methods. Crystalline structures and optical properties were investigated using X-Ray diffraction (XRD) and diffuse reflection spectroscopy, respectively. Methods: The present work has focused on the creation of a colossal (giant) dielectric permittivity (GP) behavior with the TiO2 host NCs through the Bi/W codoping to construct electronic core/shell structures. In addition, the influence of post-hydrogenation on the created GP was also examined. Results: It was found a high permittivity of 3.69×104 at 1 kHz, which was reduced to 3.29×104 by the hydrogenation of the sample. This is attributed to the densification of the itinerant electrons by the effect of the catalytic power of the doping W5+ ions to dissociate the adsorbed H2. Conclusion: The present values of GP are much higher than the permittivity of the pure TiO2 and the Bi-doped TiO2 ceramic, which was attributed to the construction of core/shell electronics structures. As a result, the doping process has been studied in detail in relation to scientific expectations.

Computation-Efficient Fault Detection Framework for Partially Known Nonlinear Distributed Parameter Systems.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Multiagent Trust Region Policy Optimization.

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents' observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.

Determination of Quercetin in Iraqi Green Walnut Husk via Green Ionic Liquid-Based Digestion Extraction and Gel Filtration Procedures-Spectrophotometry and Microfluidic Paper-Based Analytical Platform Detection

في هذه الدراسة تم استخلاص ،عزل وتقدير الكيورستين الموجود في قشور الجوز العراقي باستخدام طريقة جديدة تعتمد على الاستخلاص بطريقة الهضم المبنية على استخدام السوائل الايونية و طريقة الترشيح بالجل للفصل المقترنة مع المطيافية الضوئية للتقدير ومن ثم مقارنتها مع تقنية جديدة تسمى تقنية السوائل المايكروية الورقية مستهدفة بناء طريقة اقتصادية، صديقة للبيئة وخضراء. ولقد تم اختيار 1 بيوتيل-3-مثيل ايمادزوليم كلورايد كمذيب اخضر بديل للمذيبات التقليدية بسبب خصائصة الذوبانية .ولقد اظهرت النتائج ان 100 ملغرام من القشورتحتاج الى 15 مل من المذيب و30 دقيقة من الهضم عند درجة75°C كظروف مثلى لاعطاء افضل ناتج للمستخلص بطريقة الهضم مقارنه مع طرق الاستخلاص الاخرى .تحت هذه الظروف المثلى تم عزل الكيورستين باستخدام طريقة الترشيح بالجل وتم التاكد من المركب المعزول باستخدام تقنية كروموتوغرافيا السائل عالية الاداء و اشعة فورية تحت الحمراء .تم تقدير الكيورستين المعزول من المستخلص لونيا ( طريقة كلوريد الالمنيوم) باستخدام طريقتي المطيافية الضوئية و وتقنية السوائل المايكروية الورقية معطيا الخطية لمنحنيات المعايرة لكلتا الطريقيتين بين 10-100 مليغرام / لتر مع معامل ارتباط0.9369 و 0.7917 لكلتا الطريقتين. ومن ثم تم تقدير الفعالية المضادة للاكسدة باستخجام طريقة قوة الاختزال للمركب المعزول مظهرة فعالية مضادة للاكسدة جيدة و بين تحليل التباين (ANOVA) عدم وجود فرق كبير بين تقنية المطيافية الضوئية وتقنية السوائل المايكروية الورقية. كخلاصة يمكن اعتبار المذيب الايوني BMIMCLمذيبا امنا ومناسبا لاستخلاص المركبات النباتية ذات الخواص الطبية وكذلك فان تقنية السوائل المايكروية الورقية اثبتت مقدرتها على التحليل بكفاءة مساوية الى طرق التحليل التقليدية بالاضافة الى خصائصها الخضراء.

Recent Advances in Transition Metal Catalyzed Synthesis of C3-Substitution-free 2-Oxindole Derivatives

Abstract: 2-Oxindole unit is one of the most important scaffolds found in several alkaloids, natural products, antitumor agents, pharmaceutically important compounds, etc. Molecules containing the 2- oxindole moiety were first isolated from the cat claw plant, widely distributed in the Amazon jungle. It has now been demonstrated that these molecules are present in a wide range of chemicals derived from plant sources. The capacity of 2-oxindole to be altered by various chemical groups to provide unique biological activities can be attributed to its function as a chemical framework for creating and developing biological medications. Since the development of its first synthetic methodology, several research groups have developed protocols for producing 2-oxindole core and its bioactive derivatives. These include the traditional method and the transition/non-transition metal-catalyzed pathway for the synthesis of C3-non-substituted/C3-mono-substituted/C3-di-substituted core. Among those, C3-substitution-free 2-oxindole core synthesis is quite a challenging task, as C3-centre is very reactive. Syntheses of C3-substitution-free 2-oxindole cores have been less explored compared to other substituted 2-oxindole derivatives. In this review article, we have mainly focused on showcasing the transition metal-catalyzed synthetic methodology for the synthesis of 2-oxindoles with no substitution at C3-centre.

Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder

Background: Protein lysine crotonylation (Kcr), a newly discovered important posttranslational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmental defects and malignant transformation. Objective: Identifying Kcr sites is advantageous for the discovery of its biological mechanism and the development of new drugs for related diseases. However, traditional experimental methods for identifying Kcr sites are expensive and inefficient, necessitating the development of new computational techniques. Methods: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. Results: The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of 84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art tools. Additionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior to the current model. Conclusion: These outcomes are additional evidence that Stacking-Kcr has strong application potential and generalization performance.

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation.

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

Judgment Prediction Based on Tensor Decomposition With Optimized Neural Networks.

In the field of smart justice, handling legal cases through artificial intelligence technology is a research hotspot. Traditional judgment prediction methods are mainly based on feature models and classification algorithms. The former is difficult to describe cases from multiple angles and capture the correlation information between different case modules, while requires a wealth of legal expertise and manual labeling. The latter is unable to accurately extract the most useful information from case documents and produce fine-grained predictions. This article proposes a judgment prediction method based on tensor decomposition with optimized neural networks, which consists of OTenr, GTend, and RnEla. OTenr represents cases as normalized tensors. GTend decomposes normalized tensors into core tensors using the guidance tensor. RnEla intervenes in a case modeling process in GTend by optimizing the guidance tensor, so that core tensors represent tensor structural and elemental information, which is most conducive to improving the accuracy of judgment prediction. RnEla consists of the similarity correlation Bi-LSTM and optimized Elastic-Net regression. RnEla takes the similarity between cases as an important factor for judgment prediction. Experimental results on real legal case dataset show that the accuracy of our method is higher than that of the previous judgment prediction methods.

PSMA PET/MR is a New Imaging Option for Identifying Glioma Recurrence and Predicting Prognosis.

Glioma is characterized by a high recurrence rate, while the results of the traditional imaging methods (including magnetic resonance imaging, MRI) to distinguish recurrence from treatment-related changes (TRCs) are poor. Prostate-specific membrane antigen (PSMA) (US10815200B2, Deutsches Krebsforschungszentrum, German Cancer Research Center) is a type II transmembrane glycoprotein overexpressed in glioma vascular endothelium, and it is a promising target for imaging and therapy. The study aimed to assess the performance of PSMA positron emission tomography/ magnetic resonance (PET/MR) for diagnosing recurrence and predicting prognosis in glioma patients. Patients suspected of glioma recurrence who underwent 18F-PSMA-1007 PET/MR were prospectively enrolled. Eight metabolic parameters and fifteen texture features of the lesion were extracted from PSMA PET/MR. The ability of PSMA PET/MR to diagnose glioma recurrence was investigated and compared with conventional MRI. The diagnostic agreement was assessed using Cohen κ scores and the predictive parameters of PSMA PET/MR were obtained. Kaplan-Meier method and Cox proportional hazard model were used to analyze recurrence- free survival (RFS) and overall survival (OS). Finally, the expression of PSMA was analyzed by immunohistochemistry (IHC). Nineteen patients with a mean age of 48.11±15.72 were assessed. The maximum tumorto- parotid ratio (TPRmax) and texture features extracted from PET and T1-weighted contrast enhancement (T1-CE) MR showed differences between recurrence and TRCs (all p <0.05). PSMA PET/MR and conventional MRI exhibited comparable power in diagnosing recurrence with specificity and PPV of 100%. The interobserver concordance was fair between the two modalities (κ = 0.542, p = 0.072). The optimal cutoffs of metabolic parameters, including standardized uptake value (SUV, SUVmax, SUVmean, and SUVpeak) and TPRmax for predicting recurrence were 3.35, 1.73, 1.99, and 0.17 respectively, with the area under the curve (AUC) ranging from 0.767 to 0.817 (all p <0.05). In grade 4 glioblastoma (GBM) patients, SUVmax, SUVmean, SUVpeak, TBRmax, TBRmean, and TPRmax showed improved performance of AUC (0.833-0.867, p <0.05). Patients with SUVmax, SUVmean, or SUVpeak more than the cutoff value had significantly shorter RFS (all p <0.05). In addition, patients with SUVmean, SUVpeak, or TPRmax more than the cutoff value had significantly shorter OS (all p <0.05). PSMA expression of glioma vascular endothelium was observed in ten (10/11, 90.9%) patients with moderate-to-high levels in all GBM cases (n = 6/6, 100%). This primitive study shows multiparameter PSMA PET/MR to be useful in identifying glioma (especially GBM) recurrence by providing excellent tumor background comparison, tumor heterogeneity, recurrence prediction and prognosis information, although it did not improve the diagnostic performance compared to conventional MRI. Further and larger studies are required to define its potential clinical application in this setting.

One-Stage Shifted Laplacian Refining for Multiple Kernel Clustering.

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a "three-stage" scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the "one-stage" scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to O(n) and O(n2) , respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.

Plant Adaptation and Tolerance to Heat Stress: Advance Approaches and Future Aspects.

Heat stress impacts plant growth at all phases of development, although the particular threshold for heat tolerance varies significantly across different developmental stages. During seed germination, elevated temperatures can either impede or completely halt the process, contingent upon the plant type and the severity of the stress. During advanced stages, high temperatures can have a negative impact on photosynthesis, respiration, water balance, and membrane integrity. Additionally, they can also influence the levels of hormones and primary and secondary metabolites. In addition, during the growth and development of plants, there is an increased expression of various heat shock proteins, as well as other proteins related to stress, and the generation of reactive oxygen species (ROS). These are significant plant responses to heat stress. Plants employ several strategies to deal with heat stress, such as maintaining the stability of their cell membranes, removing harmful reactive oxygen species (ROS), producing antioxidants, accumulating and adjusting compatible solutes, activating mitogen-activated protein kinase (MAPK) and calcium-dependent protein kinase (CDPK) cascades, and, crucially, signaling through chaperones and activating transcription. These molecular-level systems boost the ability of plants to flourish in heat stress. Potential genetic methods to enhance plant heat stress resistance encompass old and modern molecular breeding techniques and transgenic approaches, all of which rely on a comprehensive comprehension of these systems. Although several plants exhibit enhanced heat tolerance through traditional breeding methods, the effectiveness of genetic transformation techniques has been somewhat restricted. The latter results from the current constraints in our understanding and access to genes that have known impacts on plant heat stress tolerance. However, these challenges may be overcome in the future. Besides genetic methods, crops' heat tolerance can be improved through the pre-treatment of plants with various environmental challenges or the external application of osmoprotectants such as glycine betaine and proline. Thermotolerance is achieved through an active process in which plants allocate significant energy to maintain their structure and function to avoid damage induced by heat stress. The practice of nanoparticles has been shown to upgrade both the standard and the quantity of produce when crops are under heat stress. This review provides information on the effects of heat stress on plants and explores the importance of nanoparticles, transgenics, and genomic techniques in reducing the negative consequences of heat stress. Furthermore, it explores how plants might adapt to heat stress by modifying their biochemical, physiological, and molecular reactions.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ∼5 and ∼25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

Machine-Learning Metacomputing for Materials Science Data

Abstract Materials science requires the collection and analysis of great quantities of data. These data almost invariably require various post-acquisition computation to remove noise, classify observations, fit parametric models, or perform other operations. Recently developed machine-learning (ML) algorithms have demonstrated great capability for performing many of these operations, and often produce higher quality output than traditional methods. However, it has been widely observed that such algorithms often suffer from issues such as limited generalizability and the tendency to “over fit” to the input data. In order to address such issues, this work introduces a metacomputing framework capable of systematically selecting, tuning, and training the best available machine-learning model in order to process an input dataset. In addition, a unique “cross-training” methodology is used to incorporate underlying physics or multiphysics relationships into the structure of the resultant ML model. This metacomputing approach is demonstrated on four example problems: repairing “gaps” in a multiphysics dataset, improving the output of electron back-scatter detection crystallographic measurements, removing spurious artifacts from X-ray microtomography data, and identifying material constitutive relationships from tensile test data. The performance of the metacomputing framework on these disparate problems is discussed, as are future plans for further deploying metacomputing technologies in the context of materials science and mechanical engineering.

Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.

Federated Learning Empowered Real-Time Medical Data Processing Method for Smart Healthcare.

Computer-aided diagnosis (CAD) has always been an important research topic for applying artificial intelligence in smart healthcare. Sufficient medical data are one of the most critical factors in CAD research. However, medical data are usually obtained in chronological order and cannot be collected all at once, which poses difficulties for the application of deep learning technology in the medical field. The traditional batch learning method consumes considerable time and space resources for real-time medical data, and the incremental learning method often leads to catastrophic forgetting. To solve these problems, we propose a real-time medical data processing method based on federated learning. We divide the process into the model stage and the exemplar stage. In the model stage, we use the federated learning method to fuse the old and new models to mitigate the catastrophic forgetting problem of the new model. In the exemplar stage, we use the most representative exemplars selected from the old data to help the new model review the old knowledge, which further mitigates the catastrophic forgetting problem of the new model. We use this method to conduct experiments on a simulated medical real-time data stream. The experimental results show that our method can learn a disease diagnosis model from a continuous medical real-time data stream. As the amount of data increases, the performance of the disease diagnosis model continues to improve, and the catastrophic forgetting problem has been effectively mitigated. Compared with the traditional batch learning method, our method can significantly save time and space resources.

A Parametric Design Method for Engraving Patterns on Thin Shells.

Designing thin-shell structures that are diverse, lightweight, and physically viable is a challenging task for traditional heuristic methods. To address this challenge, we present a novel parametric design framework for engraving regular, irregular, and customized patterns on thin-shell structures. Our method optimizes pattern parameters such as size and orientation, to ensure structural stiffness while minimizing material consumption. Our method is unique in that it works directly with shapes and patterns represented by functions, and can engrave patterns through simple function operations. By eliminating the need for remeshing in traditional FEM methods, our method is more computationally efficient in optimizing mechanical properties and can significantly increase the diversity of shell structure design. Quantitative evaluation confirms the convergence of the proposed method. We conduct experiments on regular, irregular, and customized patterns and present 3D printed results to demonstrate the effectiveness of our approach.

Breast cancer detection employing stacked ensemble model with convolutional features.

Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.

Fast Clustering by Directly Solving Bipartite Graph Clustering Problem.

Spectral clustering (SC) has been widely used in many applications and shows excellent performance. Its high computational cost limits its applications; many strategies including the anchor technique can partly alleviate the high computational cost problem. However, early methods ignore the fact that SC usually involves two stages: relaxation and postprocessing, i.e., it relaxes the discrete constraints to continuous constraints, and then conducts the postprocessing to get the discrete solution, which is time-consuming and deviates from directly solving the primal problem. In this article, we first adopt the bipartite graph strategy to reduce the time complexity of SC, and then an improved coordinate descent (CD) method is proposed to solve the primal problem directly without singular value decomposition (SVD) and postprocessing, i.e., directly solving the primal problem not approximately solving. Experiments on various real-world benchmark datasets show that the proposed method can get better solutions faster with better clustering performance than traditional optimization methods. Furthermore, it can jump out of local minima of traditional methods and continue to obtain better local solutions. Moreover, compared with other clustering methods, it also shows its superiority.

Fast Clustering via Maximizing Adaptively Within-Class Similarity.

Clustering aims to make data points in the same group have higher similarity or make data points in different groups have lower similarity. Therefore, we propose three novel fast clustering models motivated by maximizing within-class similarity, which can obtain more instinct clustering structure of data. Different from traditional clustering methods, we divide all n samples into m classes by the pseudo label propagation algorithm first, and then m classes are merged to c classes ( ) by the proposed three co-clustering models, where c is the real number of categories. On the one hand, dividing all samples into more subclasses first can preserve more local information. On the other hand, proposed three co-clustering models are motivated by the thought of maximizing the sum of within-class similarity, which can utilize the dual information between rows and columns. Besides, the proposed pseudo label propagation algorithm can be a new method to construct anchor graphs with linear time complexity. A series of experiments are conducted on both synthetic and real-world datasets and the experimental results show the superior performance of three models. It is worth noting that for the proposed models, FMAWS2 is the generalization of FMAWS1 and FMAWS3 is the generalization of other two.