Stock Prediction Based on Principal Component Analysis Principal Component Analysis and Long Term Short Term Memory
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The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors in FA was discussed in detail. A new criterion for determining the number of factors and principal components is discussed, which will allow to present most of the variance of each of the analyzed primary variables. An efficient algorithm for determining the number of factors in FA, which complies with this criterion, was also proposed. This algorithm was adapted to find the number of principal components in PCA. It was also proposed to modify the PCA algorithm using a new method of determining the number of principal components. The obtained results were discussed.
Sparse PCA
Factor Analysis
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Component analysis
Sparse PCA
Factor Analysis
Rank (graph theory)
Factor (programming language)
Component (thermodynamics)
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To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.
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Principal component analysis is the well-known method in pattern recognition, but classical principal component analysis extract some features that keep maximal scatter and the algorithm doesn't use the classificatory information of samples. Therefore, extracted features aren't very efficient to classification based on classical principal component analysis. Based on the image retrieve principle, the paper presents a kind of retrieve space principal component analysis (RS-PCA). Then, a supervised retrieve space principal component analysis (SRS-PCA) using classificatory information are developed according to RS-PCA. The algorithm makes the extracted features more effective and the recognition precision is increased. The experiments resulted on ORL and Yale face database demonstrate that the proposed algorithm has more powerful and excellent performance than classical principal component analysis.
Component (thermodynamics)
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With the aim of establishing a rapid method to discriminate Boletus tomentipes samples from different regions, FTIR spectroscopy with the aid of principal component analysis and clustering analysis were used in the present study. The information of infrared spectra of B. tomentipes samples originated from 15 regions has been collected. The original infrared spectra was pretreated by multiplicative signal correction (MSC) in combination with second derivative and Norris smooth. The spectral data were analyzed by principal component analysis and cluster analysis after the optimal pretreatment of MSC+SD+ND (15, 5), and the reasons for the differences of B. tomentipes samples from different regions could be explained through the principal component loading plot. The results showed that, the RSDs of repeatability, accuracy and stability of the method were 0.17%, 0.08% and 0.27%, respectively, which indicated the method was stable and reliable. The cumulative contribution of first three principal components of PCA was 87.24% which could reflect the most information of the samples. Principal component scores scatter plot displaying the samples from same origin could clustered together and samples from different areas distributed in a relatively independent space. Which can distinguish samples collected from different origins, effectively. The loading plot of principal component showed that with the principal component contribution rate decreasing, the captured sample information of principal component was also reducing. In the wave number of 3 571, 2 958, 1 625, 1 456, 1 405, 1 340, 1 191, 1 143, 1 084, 935, 840, 727 cm-1, the first principal component captured a large amount of sample information which attributed to carbohydrates, proteins, amino acids, fat, fiber and other chemical substances. Which showed that the different contents of these chemical substances may be the basis of discrimination of B. tomentipes samples from different origins. Cluster analysis based on ward method and Euclidean distance has shown the classification and correlation among samples. Samples originated from 15 regions could be clustered correctly in accordance with the basic origins and the correct rate was 93.33%. Which can be used to identify and analyze B. tomentipes collected from different sites. Fourier transform infrared spectroscopy combined with principal component analysis and cluster analysis can be effectively used to discriminate origins of B. tomentipes mushrooms and the reasons for the differences of B. tomentipes samples from different regions could be explained. This method could provide a reliable basis for discrimination and application of wild edible mushrooms.
Repeatability
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Білім берy қоғaмның экономикaлық дaмyының негізі, әлеyметтік тұрaқтылықтың фaкторлaрының бірі, хaлықтың рyхaни-aдaмгершілік әлеyетінің және интеллектyaлдық өсyінің қaйнaр көзі ретінде бaрлық yaқыттaрдa тaптырмaс құндылық болып есептеліп келеді. Aл қaзіргідей aдaм кaпитaлын қaлыптaстырy мен дaмытy мәселесін шешy негізгі міндет ретінде қaрaстырылaтын зaмaндa хaлықтың білімдік қaжеттіліктері өсіп, жоғaры, ортa aрнayлы, кәсіби қосымшa білім aлyғa үміткерлер сaны aртa түсyде. Бұғaн жayaп ретінде білім берy ұйымдaрының сaлaлaнyы aртып, әртүрлі типтегі оқy орындaрының сaны aртyдa, білім берyдің инфрaқұрылымы, бaсқaрy формaлaры, әдістемелік, ғылыми қызмет түрлері дaмyдa. Олaрды білім aлyшылaрдың жеке сұрaныстaры мен мүмкіндіктеріне бaғыттay күшейтілyде. Осығaн орaй білімнің сaпaсынa қойылaтын тaлaптaр aртып, бұл сaлaның әлеyметпен өзaрa әрекеттестігіне негізделген құрылымдық – қызметтік дaмyының көкейтестілігі aртyдa. Мaқaлaдa «серіктестік», «әлеyметтік серіктестік», «білімдегі әлеyметтік серіктестік» ұғым- дaрының мәні aшылып, олaрдың қaлыптaсy және дaмy үрдісіне шолy жaсaлaды, жоғaры оқy орындaрындa педaгогтaрды дaярлayдa әлеyметтік серіктестердің әлеyетін пaйдaлaнyдa бaсшылыққa aлынaтын ұстaнымдaр мен тиімді жолдaры сипaттaлaды. Түйін сөздер: серіктестік, әлеyметтік серіктестік, білімдегі әлеyметтік серіктестік, бірлескен әрекет ұстaнымдaры, әлеуметтік серіктестік әлеуеті. Обрaзовaние является основой экономического рaзвития обществa, одним из фaкторов социaль- ной стaбильности, источником дyховно-нрaвственного потенциaлa и интеллектyaльного ростa людей и во все временa считaлось незaменимой ценностью. И в нaстоящее время, когдa решение проблемы формировaния и рaзвития человеческого кaпитaлa рaссмaтривaется кaк основнaя зaдaчa, рaстyт обрaзовaтельные потребности людей, yвеличивaется количество желaющих полyчить высшее, среднее, специaльное, профессионaльное дополнительное обрaзовaние. В ответ нa это yсиливaется рaзветвленность обрaзовaтельных оргaнизaций, yвеличивaется количество обрaзовaтельных оргaни- зaций рaзличного типa, рaзвивaются инфрaстрyктyрa обрaзовaния, формы yпрaвления, методическaя и нayчнaя деятельность. Yсиливaется их ориентaция нa индивидyaльные потребности и возможности обyчaющихся. В связи с этим повышaются требовaния к кaчествy обрaзовaния, возрaстaет знaчение стрyктyрно-фyнкционaльного рaзвития этой сферы нa основе взaимодействия с обществом. В стaтье рaскрывaется знaчение понятий «пaртнерство», «социaльное пaртнерство», «социaльное пaртнерство в обрaзовaнии», рaссмaтривaется процесс их стaновления и рaзвития, описывaются рyко- водящие принципы и эффективные способы использовaния потенциaлa социaльных пaртнеров в подготовке педaгогических кaдров в высших yчебных зaведениях. Ключевые словa: партнерство, социaльное пaртнерство, социaльное пaртнерство в обрaзовaнии, принципы совместного действия, поненциал социального партнерство. Education is the basis of the economic development of society, one of the factors of social stability, a source of spiritual and moral potential and intellectual growth of people and has always been considered an irreplaceable value. And at the present time, when the solution of the problem of the formation and development of human capital is considered as the main task, the educational needs of people are growing, the number of people wishing to receive higher, secondary, special, professional additional education is increasing. In response to this, the branching of educational organizations is increasing, the number of educational organizations of various types is increasing, the infrastructure of education, forms of management, methodological and scientific activities are developing. Their focus on the individual needs and capabilities of students is increasing. In this regard, the requirements for the quality of education are increasing, the importance of the structural and functional development of this sphere on the basis of interaction with society is increasing. The article reveals the meaning of the concepts of "partnership", "social partnership", "social partnership in education", examines the process of their formation and development, describes the guidelines and effective ways to use the potential of social partners in the training of teachers in higher educational institutions. Keywords: partnership, social partnership, social partnership in education, principles of joint action, the potential of social partnership.
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ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTClassification of Vegetable Oils by Principal Component Analysis of FTIR SpectraDavid A. Rusak , Leah M. Brown , and Scott D. Martin View Author Information Department of Chemistry, University of Scranton, Scranton, PA 18510Cite this: J. Chem. Educ. 2003, 80, 5, 541Publication Date (Web):May 1, 2003Publication History Received3 August 2009Published online1 May 2003Published inissue 1 May 2003https://pubs.acs.org/doi/10.1021/ed080p541https://doi.org/10.1021/ed080p541research-articleACS PublicationsRequest reuse permissionsArticle Views2531Altmetric-Citations42LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Infrared light,Lipids,Mathematical methods,Plant derived food,Principal component analysis Get e-Alerts
Chemometrics
Plot (graphics)
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Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the original variables which can be numerous in most modern applications. To address this challenge, we first propose the use of sparse principal component analysis (SPCA) where the loadings of some variables in principal components are restricted to zero. This paper then describes a technique to determine the number of non-zero loadings in each principal component. Furthermore, we compare the performance of PCA and SPCA in fault detection. The validity and potential of SPCA are demonstrated through simulated data and a comparative study with the benchmark Tennessee Eastman process.
Sparse PCA
Benchmark (surveying)
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