Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Anal...Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.展开更多
[Objectives]Hubei Province has a superior geographical location,and is located in the middle and lower reaches of the Yangtze River,with pleasant climate and abundant natural resources.It is an important province of p...[Objectives]Hubei Province has a superior geographical location,and is located in the middle and lower reaches of the Yangtze River,with pleasant climate and abundant natural resources.It is an important province of population,agriculture and resources in China.[Methods]Based on the data of Statistical Yearbook of Hubei 2018,the agricultural economic indicators of the cities and prefectures in Hubei Province were analyzed with principal component analysis method by using SPSS19.0.[Results]The comprehensive scores and rankings of the agricultural economic development level of the 17 cities and prefectures in Hubei Province were obtained.They were divided into four agricultural development levels.[Conclusions]According to the analysis results,corresponding policy recommendations were put forward to promote the development of agricultural economy in Hubei Province.展开更多
This paper studies the comprehensive urban competitiveness and performs the principal component analysis. The results show that the comprehensive evaluation of urban competitiveness is not entirely dependent on the ci...This paper studies the comprehensive urban competitiveness and performs the principal component analysis. The results show that the comprehensive evaluation of urban competitiveness is not entirely dependent on the city's economic strength or GDP,and it is necessary to consider from resource allocation capacity,openness and public service capacity. By selecting various data concerning 11 prefecture-level cities in Jiangxi Province in 2006,2009 and 2012,this paper gets the ranking results and analyzes trends,to provide a basis for making future economic policy.展开更多
The objective of this paper was to develop a comprehensive evaluation method and index to evaluate the performance of sealants and fillers for cracks in asphalt concrete pavements using the method of principal compone...The objective of this paper was to develop a comprehensive evaluation method and index to evaluate the performance of sealants and fillers for cracks in asphalt concrete pavements using the method of principal component analysis. The performance experiments including cone penetration, softening point, flow, resilience and tension at low temperature respectively were conducted by reference of ASTM D5329 for eight sealants and fillers often used in China. There by a principal component model was developed and weight of every index was calculated. The experimental results show that there are significantly different performances for sealants and fillers often used in China. Principal component analysis is an objective method that evaluates and selects the performance of sealants and fillers for cracks in asphalt concrete pavements.展开更多
After 30 years of economic development, the high-tech industry has played </span><span style="font-family:Verdana;">an </span><span style="font-family:Verdana;">important ro...After 30 years of economic development, the high-tech industry has played </span><span style="font-family:Verdana;">an </span><span style="font-family:Verdana;">important role in China’s national economy. The development of high-level</span><span style="font-family:"font-size:10pt;"> </span><span style="font-family:Verdana;">technological industry plays a leading role in guiding the transformation of </span><span style="font-family:Verdana;">China’s economy from “investment-driven” to “technology-driven”. The</span><span style="font-family:Verdana;"> high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.展开更多
文摘Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.
文摘[Objectives]Hubei Province has a superior geographical location,and is located in the middle and lower reaches of the Yangtze River,with pleasant climate and abundant natural resources.It is an important province of population,agriculture and resources in China.[Methods]Based on the data of Statistical Yearbook of Hubei 2018,the agricultural economic indicators of the cities and prefectures in Hubei Province were analyzed with principal component analysis method by using SPSS19.0.[Results]The comprehensive scores and rankings of the agricultural economic development level of the 17 cities and prefectures in Hubei Province were obtained.They were divided into four agricultural development levels.[Conclusions]According to the analysis results,corresponding policy recommendations were put forward to promote the development of agricultural economy in Hubei Province.
文摘This paper studies the comprehensive urban competitiveness and performs the principal component analysis. The results show that the comprehensive evaluation of urban competitiveness is not entirely dependent on the city's economic strength or GDP,and it is necessary to consider from resource allocation capacity,openness and public service capacity. By selecting various data concerning 11 prefecture-level cities in Jiangxi Province in 2006,2009 and 2012,this paper gets the ranking results and analyzes trends,to provide a basis for making future economic policy.
基金Funded by the National Natural Science Foundation of China(Nos.51408287 and 51668038)the Rolls Supported by Program for Changjiang Scholars and Innovative Research Team in University(IRT_15R29)+2 种基金the Distinguished Young Scholars Fund of Gansu Province(1606RJDA318)the Natural Science Foundation of Gansu Province(1506RJZA064)the Excellent Program of Lanzhou Jiaotong University(201606)
文摘The objective of this paper was to develop a comprehensive evaluation method and index to evaluate the performance of sealants and fillers for cracks in asphalt concrete pavements using the method of principal component analysis. The performance experiments including cone penetration, softening point, flow, resilience and tension at low temperature respectively were conducted by reference of ASTM D5329 for eight sealants and fillers often used in China. There by a principal component model was developed and weight of every index was calculated. The experimental results show that there are significantly different performances for sealants and fillers often used in China. Principal component analysis is an objective method that evaluates and selects the performance of sealants and fillers for cracks in asphalt concrete pavements.
文摘After 30 years of economic development, the high-tech industry has played </span><span style="font-family:Verdana;">an </span><span style="font-family:Verdana;">important role in China’s national economy. The development of high-level</span><span style="font-family:"font-size:10pt;"> </span><span style="font-family:Verdana;">technological industry plays a leading role in guiding the transformation of </span><span style="font-family:Verdana;">China’s economy from “investment-driven” to “technology-driven”. The</span><span style="font-family:Verdana;"> high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.