scholarly journals Measuring the Performance of Hospitals in Lebanese qadas Using PCA- DEA Model

2019 ◽  
Vol 12 (1) ◽  
pp. 23
Author(s):  
Alissar Nasser
Keyword(s):  
Principal Component Analysis ◽  
Relative Efficiency ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Envelopment ◽  
Hospital Level ◽  
Discriminating Power ◽  
Input Model ◽  
Decision Making Units

We study in this paper the performance of Hospitals in Lebanon. Using the nonparametric method Data Envelopment Analysis (DEA), we are able to measures relative efficiency of Hospitals in Lebanon. DEA is a technique that uses linear programming and it measures the relative efficiency of similar type of organizations termed as Decision Making Units (DMUs). In this study, due to the lack of individual data on hospital level, each DMU refers to a qada in Lebanon where the used data represent the aggregation of input and outputs of different hospitals within the qada. In DEA, the inclusion of more number of inputs and /or outputs results in getting a more number of efficient units. Therefore, selecting the appropriate inputs and outputs is a major factor of DEA results. Therefore, we use here the Principal Component Analysis (PCA) in order to reduce the data structure into certain principal components which are essential for identifying efficient DMUs. It is important to note that we have used the basic BCC-input model for the entire analysis. We considered 24 DMUs for the study, using DEA on original data; we got 17 DMUs out of 24 DMUs as efficient. Then we considered 1 PC for inputs and 1 PC for output with almost 80 percent variances, resulting in 3 DMUs as efficient and 21 as inefficient. Using 1 PC for input and 2 PCs for output with 90 percent variance for both input and output, we got 9 DMUs as efficient and 15 DMUs as inefficient. Finally, we have attempted to identify the efficient units with 2 PCs and for 2 PCs for input and outputs with variance more than 95 percent, resulting in 10 efficient DMUs and 14 inefficient DMUs. In Principal Component analysis, if the variance lies between 80 percent to-90 percent it is judged as a meaningful one. It is concluded that Principal Component Analysis plays an important role in the reduction of input output variables and helps in identifying the efficient DMUs and improves the discriminating power of DEA.

scholarly journals Multi-Seam Coal Deposit Modeling via Principal Component Analysis & GIS

Minerals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 532
Author(s):  
Georgios Louloudis ◽  
Christos Roumpos ◽  
Konstantinos Theofilogiannakos ◽  
Nikolaos Stathopoulos
Keyword(s):  
Principal Component Analysis ◽  
Surface Mining ◽  
Principal Component ◽  
Original Data ◽  
Drill Hole ◽  
Component Analysis ◽  
Coal Deposit ◽  
Mining Operations ◽  
Maximum Variance ◽  
Mine Development

Spatial modeling and evaluation is a critical step for planning the exploitation of mineral deposits. In this work, a methodology for the investigation of a multi-seam coal deposit spatial variability is proposed. The study area includes the Klidi (Florina, Greece) multi-seam lignite deposit which is suitable for surface mining. The analysis is based on the original data of 76 exploratory drill-holes in an area of 10 km2, in conjunction with the geological and geomorphological data of the deposit. The analytical methods include drill-hole data analysis and evaluation based on an appropriate algorithm, principal component analysis and geographic information techniques. The results proved to be very satisfactory for the explanation of the maximum variance of the initial data values as well as the identification of the deposit structure and the optimum planning of mine development. The proposed analysis can be also helpful for minimizing cost and optimizing efficiency of surface mining operations. Furthermore, the provided methods could be applied in other areas of geosciences, indicating the theoretical value as well as the important practical implications of the analysis.

Damage detection in nonlinear civil structures using kernel principal component analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 2414-2430
Author(s):  
Khaoula Ghoulem ◽  
Tarek Kormi ◽  
Nizar Bel Hadj Ali
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Model ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Correlated Data ◽  
Data Driven ◽  
Kernel Principal Component Analysis ◽  
Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

scholarly journals INTERNAL BENCHMARKING IN RETAILING WITH DEA AND GIS: THE CASE OF A LOYALTY-ORIENTED SUPERMARKET CHAIN

2020 ◽  
Vol 21 (4) ◽  
pp. 1035-1057
Author(s):  
Amparo Baviera-Puig ◽  
Tomás Baviera ◽  
Juan Buitrago-Vera ◽  
Carmen Escribá-Pérez
Keyword(s):  
Principal Component Analysis ◽  
Data Envelopment Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Data Envelopment ◽  
Classification Analysis ◽  
On Demand ◽  
Relative Measure ◽  
Trade Area ◽  
Internal Benchmarking

Data Envelopment Analysis (DEA) is a relative measure of efficiency applied to a set of decision units and is being used more and more frequently in the supermarket sector. Nonetheless, given how strongly the sector’s financials depend on demand, companies need to combine this measurement with trade area information to best manage corporate efficiency. In this paper, the proposal consists of integrating DEA with a clearly articulated, structural typology so that supermarkets, based on their particular characteristics, can determine which variables are most critical for improving their efficiency. This methodology has been validated in the case of one of Spain’s five largest supermarket chains. A principal component analysis and a classification analysis were carried out on a series of internal management variables from 61 locations for which DEA had been used to calculate efficiency and to which multiple trade area variables were added using GIS. Some of them are related to the loyalty scheme membership programme. These latter variables described the implantation of the loyalty scheme member programme and were revealed as key elements for the efficiency of the supermarket. This methodology provides marketing profiles that are more adapted to local circumstances, thus allowing companies to set better internal benchmarking objectives.

scholarly journals PENGELOMPOKKAN DATA WAJAH MENGGUNAKAN METODE AGGLOMERATIVE CLUSTERING DENGAN ANALISIS KOMPONEN UTAMA

2011 ◽  
Vol 15 (1) ◽  
pp. 178
Author(s):  
Altien J Rindengan ◽  
Deiby Tineke Salaki
Keyword(s):  
Principal Component Analysis ◽  
Data Clustering ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Matlab Program

PENGELOMPOKKAN DATA WAJAH MENGGUNAKAN METODE AGGLOMERATIVE CLUSTERING DENGAN ANALISIS KOMPONEN UTAMA Altien J. Rindengan1) dan Deiby Tineke Salaki1) 1)Program Studi Matematika FMIPA Universitas Sam Ratulangi Manado 95115 ABSTRAK Pada penelitian ini dilakukan analisis pengelompokkan data wajah dengan analisis komponen utama untuk mengambil beberapa akar ciri yang cukup mewakili data tersebut dan pengelompokkannya menggunakan metode agglomerative clustering. Dengan menggunakan program Matlab, data wajah yang terdiri dari 6 orang dengan 10 image dapat dikelompokkan sesuai data aslinya.  Pengelompokkannya cukup menggunakan 3 akar ciri pada selang 68 %. Kata kunci: agglomerative clustering, analisis komponen utama, data wajah  FACE DATA CLUSTERING USING AGGLOMERATIVE CLUSTERING METHODS WITH PRINCIPAL COMPONENT ANALYSIS ABSTRACT In this research, face data is grouped using principal component analysis by getting some of its eigenvalues which are representative enough to describe the data and then by using agglomerative clustering the data is clustered.  By running the Matlab program, face data which is consist of 6 people with 10 images can be clustered to fit the original data.  The clustering is enough using 3 eigenvalues with 68 % of interval. Keywords: agglomerative clustering, principal component analysis, face data

scholarly journals Initial Dataset Dimension Reduction Using Principal Component Analysis

2020 ◽  
Vol 23 ◽  
pp. 41-44
Author(s):  
Oļegs Užga-Rebrovs ◽  
Gaļina Kuļešova
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Initial Data ◽  
Dimension Reduction ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Preliminary Processing ◽  
Implicit Form ◽  
Initial Dataset

Any data in an implicit form contain information of interest to the researcher. The purpose of data analysis is to extract this information. The original data may contain redundant elements and noise, distorting these data to one degree or another. Therefore, it seems necessary to subject the data to preliminary processing. Reducing the dimension of the initial data makes it possible to remove interfering factors and present the data in a form suitable for further analysis. The paper considers an approach to reducing the dimensionality of the original data based on principal component analysis.

scholarly journals Dimensionality Reduction with Sparse Locality for Principal Component Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Pei Heng Li ◽  
Taeho Lee ◽  
Hee Yong Youn
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Reconstruction Error ◽  
Dimensional Representation ◽  
Weighted Matrix ◽  
Low Dimensional

Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.

Privacy Preserving Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets

2012 ◽  
pp. 238-271
Author(s):  
Xin Li
Keyword(s):  
Principal Component Analysis ◽  
Privacy Protection ◽  
Genomic Data ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Privacy Preserving ◽  
Gene Clustering ◽  
Gene Profiles ◽  
Heterogeneous Datasets

In this paper, we present approaches to perform principal component analysis (PCA) clustering for distributed heterogeneous genomic datasets with privacy protection. The approaches allow data providers to collaborate together to identify gene profiles from a global viewpoint, and at the same time, protect the sensitive genomic data from possible privacy leaks. We then further develop a framework for privacy preserving PCA-based gene clustering, which includes two types of participants: data providers and a trusted central site (TCS). Two different methodologies are employed: Collective PCA (C-PCA) and Repeating PCA (R-PCA). The C-PCA requires local sites to transmit a sample of original data to the TCS and can be applied to any heterogeneous datasets. The R-PCA approach requires all local sites have the same or similar number of columns, but releases no original data. Experiments on five independent genomic datasets show that both C-PCA and R-PCA approaches maintain very good accuracy compared with the centralized scenario.

scholarly journals Classification of reduction invariants with improved backpropagation

2002 ◽  
Vol 30 (4) ◽  
pp. 239-247
Author(s):  
S. M. Shamsuddin ◽  
M. Darus ◽  
M. N. Sulaiman
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Original Data ◽  
Component Analysis ◽  
Classification Problems ◽  
Share Prices ◽  
Data Space ◽  
Backpropagation Network

Data reduction is a process of feature extraction that transforms the data space into a feature space of much lower dimension compared to the original data space, yet it retains most of the intrinsic information content of the data. This can be done by using a number of methods, such as principal component analysis (PCA), factor analysis, and feature clustering. Principal components are extracted from a collection of multivariate cases as a way of accounting for as much of the variation in that collection as possible by means of as few variables as possible. On the other hand, backpropagation network has been used extensively in classification problems such as XOR problems, share prices prediction, and pattern recognition. This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information present in moment invariants of handwritten digits, leaving the redundant information behind. Higher order centralised scale- invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification purposes.

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