Consensus Kernel K

Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view’s incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and imputation into a unified learning framework. While its framework is elegant, we observe that it overlooks the consistency between views, which leads to a reduction in the clustering performance. In order to address this issue, we propose a new unified learning method for incomplete multiview clustering, which simultaneously imputes the incomplete views and learns a consistent clustering result with explicit modeling of between-view consistency. More specifically, the similarity between each view’s clustering result and the consistent clustering result is measured. The consistency between views is then modeled using the sum of these similarities. Incomplete views are imputed to achieve an optimal clustering result in each view, while maintaining between-view consistency. Extensive comparisons with state-of-the-art methods on both synthetic and real-world incomplete multiview datasets validate the superiority of the proposed method

Incomplete Multiview Clustering via Late Fusion

In real-world applications of multiview clustering, some views may be incomplete due to noise, sensor failure, etc. Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views. However, these studies overlook the fact that clustering results with the visible instances in each view could be reliable under the random missing assumption; accordingly, it seems that learning a final clustering decision via late fusion of the clustering results from incomplete views would be more natural. To this end, we propose a late fusion method for incomplete multiview clustering. More specifically, the proposed method performs kernel k-means clustering on the visible instances in each view and then performs a late fusion of the clustering results from different views. In the late fusion step of the proposed method, we encode each view’s clustering result as a zero-one matrix, of which each row serves as a compressed representation of the corresponding instance. We then design an alternate updating algorithm to learn a unified clustering decision that can best group the visible compressed representations in each view according to the k-means clustering objective. We compare the proposed method with several commonly used imputation methods and a representative early fusion method on six benchmark datasets. The superior clustering performance observed validates the effectiveness of the proposed method

Vibration characteristics research on HTS magnetic levitation system based on a strong-coupled multi-physics model

International audienc

A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study

<div><p>Background</p><p>Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.</p><p>Methods and Findings</p><p>This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index.</p><p>Results</p><p>Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86–0.89) and (0.89; 95% CI 0.88–0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88–0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).</p><p>Conclusions/Significance</p><p>We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.</p></div

Diagnostic and Predictive Values of the Xiang-ya Risk Model at Different Cutoff Points for Predicted Probability.

<p>Diagnostic and Predictive Values of the Xiang-ya Risk Model at Different Cutoff Points for Predicted Probability.</p

Comparison of the Receiver Operating Characteristic Curves for the Xiang-ya Model and the Model Proposed by Kate et al 2014 for Predicting Cardiac Surgery-related AKI (n = 2101).

<p>AUC, area under the receiver operating characteristic curve.</p

Xiany-ya Risk Model Calculation Formula.

<p>Xiany-ya Risk Model Calculation Formula.</p

Receiver Operating Characteristic Curves for the Xiang-ya Model of Surgery-related AKI in the Training and Validation Sets (A) and in the Training and Cross-Validation Set (B).

<p>AUC, area under the receiver operating characteristic curve.</p

Baseline Characteristics of Subjects in the Training and Validation Cohorts.

<p>Baseline Characteristics of Subjects in the Training and Validation Cohorts.</p