Principal component analysis-based sports dance development influence factors research

Transcription

1 Availale online Journal of Cheical and Pharaceutical Research, 014, 6(7): Research Article ISSN : CODEN(USA) : JCPRC5 Principal coponent analysis-ased sports dance developent influence factors research Xiaoping Xie Pulic Sports Departent, Jingchu University of Technology, Jingen, Huei, China ABSTRACT Nowadays aterial civiliation and spiritual civiliation of huan eings have een rapidly developing; sports dance has ecoe a sports event that is favored y people. Due to China covers a vast geographic area and has a large population, sports dance developent suffers any factors influence as econoic level is liited, people acceptance levels are not enough and so on.when research on sports dance developent relative proles, ecessive such factors will lead to inconvenience in researching. The paper taes sports dance developent as research oects, taes teachers, referees, athletes three types of population questionnaire survey results as evidence, targeted at econoic level, copetition syste, scientific research level and other thirteen influence factors to ae principal coponent analysis. Analysis result indicates that two ain coponents can replace the thirteen influence factors. The two principal coponents are three influence factors linear coination, the differences etween the two is that every factor weight is different. Key words: sports dance, influence factors, principal coponent analysis, linear coination, weight INTRODUCTION In recent years, sports dance has een developed in lots of universities, and ecoe one of well-received sports events aong nuerous university students. To this day, sports dance developent is still not alanced. The prole leads to people to thin aout sports dance developent constraint factors. In 010, Cheng Wei-Hai in the article Heei province sports dance developent status investigation and developent countereasures research, analyed Heei province sports dance developent status, result showed that in the aspect of sports dance, Heei province en athletes were fewer, coaches and referees cultural levels needed to iprove. Copetition organiations were disordered, anageent was poor, and these factors serious restricted Heei province sports dance developent [1-3]. In 004, Zheng Chuan-Feng and others in the article Research on sports dance developent and countereasures in universities, they ade coprehensive analysis of present sports dance developent status in universities. Result showed, each university sports dance developent levels were different, students interests and positivity in sports dance event were higher, ut overall they presented as acward of theoretical nowledge and scientific researches [4-7]. With respect to this, authors provided countereasures. In order to let sports dance to e ale to etter develop, author pointed out, it should propel to sports dance optional course and clu-oriented [8-10]. In 01, Bi Fei in the article University sports dance event education research, applied teaching eperient ethod and others ultiple research ethods, analyed universities sports dance teaching transforing towards sports dance education issues, result showed that sports dance education functions were not only letting students to aster sports dance asic otions, ut also can let students attainents and quality to e iproved [10-1]. Sports dance event teaching confored to the trends of ties that were worth prooting to each university. In 003, Zhao Li in the article Chinese sports dance organiation status and counter easure research, applied ultiple research ethods, researched on sports dance developent influence factors, research result showed Chinese sports dance laced of self tetoo syste, 970

2 Xiaoping Xie J. Che. Phar. Res., 014, 6(7): scientific research levels were lower, referees grade evaluation syste was not noralied, referees education degrees were generally lower [13]. The paper taes Chinese university sports dance developent status as research oect, analyes teachers, referees and athletes each ind of situations, and further gets conclusion. MODEL ESTABLISHMENT The odel researches on sports dance developent influence factors, taes each factor teachers occupied percentage, referee occupied percentage and athlete occupied percentage as evidence, and reduces ultiple influence factors into fewer influence factors so as to easy for the ind of proles late researching. Tale 1 data is fro Chinese sports dance developent status investigation and countereasure research. Tale 1: Original data tale Influence factor Teacher Percentage% Referee Percentage % Athlete Percentage % Ran Econoic level constraints Copetition and referee syste Teachers level Funding issue Related to non-olypic Gaes events Fewer international echange Pulic concept Scientific research level Sports level Mass edia influence Disordered organiational anageent Field facilities Others Main thought of principal coponent analysis is variale s diension reduction. It is a statistical analysis ethod that transfors ultiple variales into fewer ain variales. It generally is used to data copression, syste evaluation, regression analysis and weighted analysis so on. Principal coponent analysis ethod Main way of principal coponent analysis is reducing diension of variales, which is recoining original any variales with correlation into a group of uncorrelated variales to replace original variales. Therefore, we can pay attention to every tie oservation s variales that have aiu variation, to every tie oservation s sall changed variales that can e used as constant to process and get rid of the, so that it reduces variales nuer in prole that needs to e considered. Assue that there is pieces of original indicators to do principal coponent analysis, which are recorded as,,, 1 L, now it has n ( i n) pieces of saples, corresponding oservation value is i = 1,, L, = 1,, L, taes standardiation transforation, and then transfor into, that:, and = s, = 1,, L, (1) Aong the, and standard deviation is 1. s are respectively average nuer and standard deviation, average nuer is 0, According to each saple original indicator oservation value i or after standardiation oservation value i, it solves coefficient, estalish indicator that is transfored through standardiation to epress coprehensive indicator equation coprehensive indicator = :, which can also estalish equation that uses original indicator to epress 971

3 Xiaoping Xie J. Che. Phar. Res., 014, 6(7): a ~ = () There are two requireents on defining : (1) Coprehensive indicators are utual independent fro each other or uncorrelated. () Every coprehensive indicator reflected each saple gross inforation content is equal to corresponding feature vector( coprehensive indicator coefficient)feature values. In general, it is required that selected coprehensive indicator feature vales contriution ratios su to e aove 80%. Principal coponent analysis general steps (1) According to oserved data, calculate s and (, = 1,, L, ) () By correlation coefficient atri R, it can get feature value and each principal coponent variance contriution contriution ratio and accuulative contriution ratio, and define principal coponent reserved nuer p with accuulative contriution ratio as evidence. (3) pieces of asic equations are as following:. λ ( = 1,, L,) r11 1 r1 1 L r 11 ( ) ( ) ( ) + r 1 ( ) ( ) ( ) + r ( ) ( ) ( ) + r Aong the, + L+ r + L+ r + L+ r 1 = 1,, L,. = λ ( ) 1 = λ ( ) = λ ( ) Proceed with Schidt orthogonaliation, for every ( = 1,, L,) ( ), and then let: ( ) λ i, solve its asic equations solution 1, ( ) (3),, = ( ) ( ) ( ) It can get epressed y 1,,, epressed y 1,,, principal coponent principal coponent ~ = = + a. = s, or input (4) and then get (4)Input 1,,, oserved values into principal coponent epressions, calculate each coponent value. (5) Calculate original indicator and principal coponent correlation coefficient that is also factor loading that use it to eplain principal coponent significances. Tale represents every variale counalities result. Tale s left side represents every variale eplainale variance fro all factors, while the right side represents variale counalities. Fro tale data, we can see that variale counalities are 1 that are very high, which shows ost inforation in variales can e etracted y factors; it shows the analysis is valid. 97

4 Xiaoping Xie J. Che. Phar. Res., 014, 6(7): Tale : Variales counalities tale Initial Etract Econoic level constraints Copetition and referee syste Teachers level Funding With non-olypic Gaes events International echange Pulic concept Scientific research level Sports level Mass edia influence Disordered organiational anageent Field facilities Others Etract ethod: principal coponent analysis. Tale 3: Factor contriution ratio tale Coponent Initial feature value Etract squares su and load in Rotate squares su and load in Total Variance % Accuulation % Total Variance % Accuulation % Total Variance % Accuulation % E E E E E E E E E E E E E E E E E E E E E E Etract ethod: principal coponent analysis. In Tale 3, accuulation ites data indicates percentage of total feature values. Fro tale data, it can easily see that factor 1 and factor feature values are aove 1, and the two factors feature values su are 100% of total feature values. Therefore, we use factor 1 and factor as ain factors. Tale 4: Rotational factor loading tale Coponent Etract ethod: principal coponent. Rotational ethod: Orthogonal rotation ethod with Kaiser standardiation. a. Convergent after three ties iterating of rotation. Data in Tale 4 indicates factor loading value after using Kaiser standard orthogonal rotation. By such rotating, every factor s significance is relative clear. Fro the tale, it can see that two ain factors are etracted. Fro Figure 1, it can ore intuitive indicate. 973

5 Xiaoping Xie J. Che. Phar. Res., 014, 6(7): Fig.1: Scree plot Figure 1 is feature values scree plot. In general, the figure shows ig factor steep slope and surplus factor gentle tail has ovious interruption. Generally selected ain factors are in the very steeply slope, and factors lie in gentle slope have insignificant effects on total. Fro Figure 1, it is clear that the forer two factors are in the relative steeply slope, and starts fro the third factor, the slope turns to e gentle, while starts fro the third factor, the slope is nearly ero, therefore select two factors as coprehensive factors. Fig.: The view of the rotating coponents of the space, 8 Fro Figure, it is clear that principal coponent analysis totally etracts two ain factors this tie, 9 that get closer to coordinate ais have ig factor loading and eplanatory aility is relative strong. After defining ain factors aount, it should calculate feature vectors, feature vectors aount is the sae as ain factors aount. Feature vector atri is as Tale 5 show. Tale 5: Feature vector atri F1 F By Tale 5 feature vectors, it can get principal coponent coputational forula: 974

6 Xiaoping Xie J. Che. Phar. Res., 014, 6(7): = = (5) (6) Respectively input original data into forula (5) (6), it can get data as Tale 6. Tale 6: Main coponents variales Tale 6 is two ain coponents variales after factor analysis. 1, are econoic restriction level and other thirteen factors linear coinations. That is principal coponent analysis reducing original thirteen factors into two factors so that is easy to research, ut physical significances after factor diensions reducing is hard to define. Fig.4: Principal coponent coparison chart Figure 4 is principal coponent coparison chart. Fro Figure 4, it is clear that to teachers and referees, principal coponent 1 occupied weight is igger than that of principal coponent, to athletes; principal coponent 1 occupied weight is saller than that of principal coponent. CONCLUSION Utilie principal coponent analysis thought to use fewer variales to replace original ultiple variales, these fewer variales can reflect original data ost inforation. In addition, the odel ore focuses on inforation coprehensive evaluation. The ethod also has certain drawacs, such as, when principal coponent factor loading positive and negative syols are siultaneously eisting, evaluation function significances will not e clear, naing clarity will e low, only involve a group of variales correlations. The odel s principal coponent is coposed of original factors linear coinations, so principal coponents actual significances are hard to define, ust functions as diension reduction. Principal coponent analysis application field is very widely, such as regional water resources carrying capacity prole, Town land evaluation prole, 3G networ coprehensive perforance evaluation prole and other aspects analysis proles. The paper applies principal coponent analysis into sports dance developent restriction factors, succeeds in reducing three influence factors into two principal coponents, and is convenient for later such inds of proles researching. Acnowledgent The paper elongs to one of Huei province science of education the 1th five years proect issue in 013- Regular institution of higher learning sports classroo interactive teaching research (No.013B00) research attainents. REFERENCES [1] Wang Xiaoguang. Research On Developent, 007, (5), [] CHEN Nan - yue. Journal of Yunnan Finance and Trade Institute, 005, 1(6),

7 Xiaoping Xie J. Che. Phar. Res., 014, 6(7): [3] LIU Chang-ing, GUAN Bin. Journal of Tianin University(Social Sciences), 009, 11(5), [4] Fan Liwei, Liang Jiyao. Value Engineering, 006, 5(5), [5] SUN Yi-ai. Journal of Anhui University(Philosophy & Social Sciences), 006, 30(6). [6] LU Jian-hua. Journal of Anhui University(Philosophy & Social Sciences), 006, 30(6), 5-6. [7] XU Xiao-yue. Journal of Anhui University(Philosophy & Social Sciences), 006, 30(6), 6-8. [8]SU Bao-ei, LIU Zong-ian, Liu Chang-ing. Journal of Jinan University, 00, 1(5), [9] Zhang B.; Zhang S.; Lu G.. Journal of Cheical and Pharaceutical Research, 013, 5(9), [10] Zhang B.; International Journal of Applied Matheatics and Statistics, 013, 44(14), [11] Zhang B.; Yue H.. International Journal of Applied Matheatics and Statistics, 013, 40(10), [1] Zhang B.; Feng Y.. International Journal of Applied Matheatics and Statistics, 013, 40(10), [13] Bing Zhang. Journal of Cheical and Pharaceutical Research, 014, 5(),