Low Rank Correlation Representation and Clustering

Gao, Wenyun; Dai, Sheng Hui; Abhadiomhen, Stanley Ebhohimhen; He, Wei; Yin, Xinghui

doi:10.1155/2021/6639582

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…However, these methods are not efficient for our approach because of the correlational nature of our data within clusters. Clustered data naturally have depedence due to the reason that they share common property [29]; for example, people who come from the same region have shared culture and traditions and mostly share common medical facilities.…”

Section: Methodsmentioning

confidence: 99%

Tweedie Model for Predicting Factors Associated with Distance Traveled to Access Inpatient Services in Kenya

Mwenda

Kosgei

Kerich

et al. 2022

Journal of Probability and Statistics

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Aim. This study aims to examine which factors influence the distance traveled by patients for inpatient care in Kenya. Methods. We used data from the fourth round of the Kenya Household Health Expenditure and Utilization survey. Our dependent variable was the self-reported distance traveled by patients to access inpatient care at public health facilities. As the clustered data were correlated, we used the generalized estimating equations approach with an exchangeable correlation under a Tweedie distribution. To select the best-fit covariates for predicting distance, we adopted a variable selection technique using the Q I C u and R 2 criteria, wherein the lowest (highest) value for the former (latter) is preferred. Results. Using data for 451 participants from 47 counties, we found that three-fifths were admitted between 1 and 5 days, two-thirds resided in rural areas, and 90% were satisfied with the facilities’ service. Wealth quintiles were evenly distributed across respondents. Most admissions (81%) comprised < 15, > 65, and 25–54 years. Many households were of medium size (4–6 members) and had low education level (48%), and nine-tenths had no access to insurance. While two-thirds reported employment-based income, the same number reported not having cash to pay for inpatient services; 6 out of 10 paid over 3000 KES. Thus, differences in employment, ability to pay, and household size influence the distance traveled to access government healthcare facilities in Kenya. Interpretation. Low-income individuals more likely have large households and live in rural areas and, thus, are forced to travel farther to access inpatient care. Unlike the unemployed, the employed may have better socioeconomic status and possibly live near inpatient healthcare facilities, thereby explaining their short distances to access these services. Policymakers must support equal access to inpatient services, prioritize rural areas, open job opportunities, and encourage smaller families.

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Section: Methodsmentioning

confidence: 99%

Tweedie Model for Predicting Factors Associated with Distance Traveled to Access Inpatient Services in Kenya

Mwenda

Kosgei

Kerich

et al. 2022

Journal of Probability and Statistics

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“…4.5. Convergence Study.Based on established standards of inexact augmented Lagrange multiplier (IALM) optimization strategy in several pieces of literature[5,[49][50][51], the objective function values of the J, Z, and E subproblems were expected to decrease monotonically in each iteration until convergence. Besides, the Z subproblem is known to have a closed-form solution…”

mentioning

confidence: 99%

Recursive Sample Scaling Low-Rank Representation

Gao

Li²,

Dai³

et al. 2021

Journal of Mathematics

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The low-rank representation (LRR) method has recently gained enormous popularity due to its robust approach in solving the subspace segmentation problem, particularly those concerning corrupted data. In this paper, the recursive sample scaling low-rank representation (RSS-LRR) method is proposed. The advantage of RSS-LRR over traditional LRR is that a cosine scaling factor is further introduced, which imposes a penalty on each sample to minimize noise and outlier influence better. Specifically, the cosine scaling factor is a similarity measure learned to extract each sample’s relationship with the low-rank representation’s principal components in the feature space. In order words, the smaller the angle between an individual data sample and the low-rank representation’s principal components, the more likely it is that the data sample is clean. Thus, the proposed method can then effectively obtain a good low-rank representation influenced mainly by clean data. Several experiments are performed with varying levels of corruption on ORL, CMU PIE, COIL20, COIL100, and LFW in order to evaluate RSS-LRR’s effectiveness over state-of-the-art low-rank methods. The experimental results show that RSS-LRR consistently performs better than the compared methods in image clustering and classification tasks.

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