Applying discrete PCA in data analysis

With the wide application of Internet in almost all fields, it has become the most important way for information publication, providing a large number of channels for spreading public opinion. Public opinions, as the response of Internet users to the information such as social events and government policies, reflect the status of both society and economics, which is highly valuable for the decision-making and public relations of enterprises. At present, the analysis methods for Internet public opinion are mainly based on discriminative approaches, such as Support Vector Machine (SVM) and neural network. However, when these approaches analyze the sentiment of Internet public opinion, they are failed to exploit information hidden in text, e.g. topic. Motivated by the above observation, this paper proposes a detection method for public sentiment based on Probabilistic Latent Semantic Analysis (PLSA) model. PLSA inherits the advantages of LSA, exploiting the semantic topic hidden in data. The procedure of detecting the public sentiment using this algorithm is composed of three main steps: (1) Chinese word segmentation and word refinement, with which each document is represented by a bag of words; (2) modeling the probabilistic distribution of documents using PLSA; (3) using the Z-vector of PLSA as the features of documents and delivering it to SVM for sentiment detection. We collect a set of text data from Weibo, blog, BBS etc. to evaluate our proposed approach. The experimental results shows that the proposed method in this paper can detect the public sentiment with high accuracy, outperforming the state-of-the-art approaches, i.e., word histogram based approach. The results also suggest that, text semantic analysis using PLSA could significantly boost the sentiment detection.

A novel method is proposed for updating an already trained asymmetric and symmetric probabilistic latent semantic analysis (PLSA) model within the context of a varying document stream. The proposed method is coined online PLSA (oPLSA). The oPLSA employs a fixed-size moving window over a document stream to incorporate new documents and at the same time to discard old ones (i.e., documents that fall outside the scope of the window). In addition, the oPLSA assimilates new words that had not been previously seen (out-of-vocabulary words), and discards the words that exclusively appear in the documents to be thrown away. To handle the new words, Good-Turing estimates for the probabilities of unseen words are exploited. The experimental results demonstrate the superiority in terms of accuracy of the oPLSA over well known PLSA updating methods, such as the PLSA folding-in (PLSA fold.), the PLSA rerun from the breakpoint, the quasi-Bayes PLSA, and the Incremental PLSA. A comparison with respect to the CPU run time reveals that the oPLSA is the second fastest method after the PLSA fold. However, the better accuracy of the oPLSA than that of the PLSA fold. pays off the longer computation time. The oPLSA and the other PLSA updating methods together with online LDA are tested for document clustering and F1 scores are also reported.

To determine retrospectively the role of endorectal magnetic resonance in the staging of prostate cancer. The aim of the study was to assess whether it is possible to identify a group of patients with prostate cancer, chosen for certain prognostic factors, eligible for radiotherapy that could take advantage of endorectal magnetic resonance in staging and therapy management. Between January 2002 and December 2005, 143 patients with biopsy proven prostate cancer underwent endorectal magnetic resonance. All patients were initially evaluated considering the following prognostic factors: serum prostate-specific antigen at diagnosis, Gleason score, histological grade, involvement of the seminal vesicle and extracapsular extension using the Roach III and ECE equations. The findings were then compared to the results of endorectal magnetic resonance. The relationship between the variable post-endorectal magnetic resonance stage modification and Gleason score was statistically significant (P = 0.02847). In addition, our study showed a statistically significant correlation between the risk of seminal vesicle involvement according to the Roach III formula and post-endorectal magnetic resonance stage modification (P = 0.01305). Conversely, statistical analysis showed no significant correlation between post-endorectal magnetic resonance stage modification and prostate-specific antigen values (P = 0.83440) or between post-endorectal magnetic resonance stage modification and the risk of extracapsular extension according to the extracapsular extension formula (P = 0.42748). Our data suggest that endorectal magnetic resonance could be used for staging of the subgroup of patients at high risk of seminal vesicle involvement (> 15%). Although we found a statistical correlation between Gleason score and post-endorectal magnetic resonance stage modification, statistical analysis showed no correlation between any of the subgroups. Therefore, it is not possible at the moment to identify a subgroup of patients by Gleason score that may benefit from endorectal magnetic resonance. In our opinion, extracapsular extension values were not useful to select patients for endorectal magnetic resonance.

Due to the vast growth of data collections, the statistical document modeling has become increasingly important in language processing areas. Probabilistic latent semantic analysis (PLSA) is a popular approach whereby the semantics and statistics can be effectively captured for modeling. However, PLSA is highly sensitive to task domain, which is continuously changing in real-world documents. In this paper, a novel Bayesian PLSA framework is presented. We focus on exploiting the incremental learning algorithm for solving the updating problem of new domain articles. This algorithm is developed to improve document modeling by incrementally extracting up-to-date latent semantic information to match the changing domains at run time. By adequately representing the priors of PLSA parameters using Dirichlet densities, the posterior densities belong to the same distribution so that a reproducible prior/posterior mechanism is activated for incremental learning from constantly accumulated documents. An incremental PLSA algorithm is constructed to accomplish the parameter estimation as well as the hyperparameter updating. Compared to standard PLSA using maximum likelihood estimate, the proposed approach is capable of performing dynamic document indexing and modeling. We also present the maximum a posteriori PLSA for corrective training. Experiments on information retrieval and document categorization demonstrate the superiority of using Bayesian PLSA methods.

Probabilistic Latent Semantic Analysis (PLSA) is one of the latent topic models and it has been successfully applied to visual recognition tasks. However, PLSA models have been learned mainly in batch learning, which can not handle data that arrives sequentially. In this paper, we propose a novel on-line learning algorithm for learning the parameters of PLSA. Our contributions are two-fold: (i) an on-line learning algorithm that learns the parameters of a PLSA model from incoming data; (ii) a codebook adaptation algorithm that can capture the full characteristics of all the features during the learning. Experimental results demonstrate that the proposed algorithm can handle sequentially arriving data that batch PLSA learning cannot cope with, and its performance is comparable with that of the batch PLSA learning on visual recognition.

Abstract. This research aims to (1) Analyze the chemistry handbooks used in the vocational school of Pharmacy in North Sumatra, (2) Develop a sequence of chemical material suitable to grade X semester I and systematically according to KTSP of Pharmacy Vocational School, (3) Knowing what chemical materials worth and the right to be taught in Pharmacy vocational school grade X semester I so that learning is more systematic and easily understood by BSNP students in learning, (4) Developing the chemistry handbook for grade X semester I Pharmacy Vocational School appropriate with KTSP, and (5) Knowing the opinion of teachers of chemistry at Pharmacy Vocational School on the development of standard chemistry handbook of grade X semester I. Sample in this study are two publishers of chemistry handbooks used in grade X semester I of the most widely used in Pharmacy Vocational School in North Sumatera, has graduated from the book center, and marketed nationally and four vocational school chemistry teacher used grade X Pharmaceuticals as a respondent and has teaching experience in grade X Pharmacy Vocational School for a minimum of two years and a minimum education is bachelor degree (S1). This is a research development using qualitative data described by using the time sentence by emphasizing the documentary technique. This research instrument is a questionnaire with a book containing sheets of the feasibility of the indicators derived from the assessment of National Education Standards Agency (BSNP) in 2006, later identified occurrence for each book. Furthermore it is developing chemistry handbook for grade X semester I Pharmaceuticals appropriate vocational curriculum, which will be assessed by the respondent chemistry teachers of Pharmacy Vocational School. The assessment results of validation between the average range to determine whether or not worthy of the book used in grade X semester I of Pharmacy Vocational School. The results of the feasibility analysis of the contents of the handbooks most widely used chemical in the first semester of the grade X is obtained percentage Pharmacy Vocational School: books A, and B are, respectively 51.89% and 76.01%. Validation range obtained in the proposed grade X semester I of chemical materials Pharmacy Vocational School is 3.6 means that the proposal put forward appropriate materials are used in the development of chemistry handbooks for grade X semester I Pharmacy Vocational School. Assessment chemistry handbook for grade X semester I Pharmacy Vocational School on the feasibility of content, language, and presentation by chemistry teacher of Pharmacy Vocational School respondents obtained an average yield of the range of validation in order are 3,31; 3,52, and 3,51. Validation of the overall range obtained from the four respondents chemistry teacher of Pharmacy Vocational School is 3.45, this means that the Chemistry Handbook for Grade X Semester I Pharmacy Vocational School According With KTSP is valid and does not need to be revised again and have been feasible to use in chemistry learning in Pharmacy Vocational School. Keywords: Chemistry Handbook, Pharmacy Vocational School, Analysis, Development, KTSP

Introduction: As contemporary as it looks to young people between 16 and 18 years of age, early sexual activity has its high price. Along with the strong emotions of falling in love, there come unwanted pregnancy and sexually transmitted diseases. These diseases are characterized by diverse clinical symptoms but they have the same mechanism of transmission and spread of infection - sexual contact. The aim of this study is to investigate the awareness of students from 9th to 12 th grade from the Professional School of Natural Sciences - `Acad. N. Obreshkov` - Razgrad of the risks, prevalence and the prevention of STDs and unwanted pregnancy. Materials and Methods: The study covered high-school students from 9th to 12 th grade from the Professional School of Natural Sciences - `Acad. N. Obreshkov` - Razgrad in January 2016. A hundred students participated in the survey. There were 52 girls and 48 boys. A poll with 14 questions was developed for this study - 12 multiple choice questions and 2 open-ended questions. The processing and data analysis used documentary, statistical and graphical methods. Results: Students from the 9 th grade were chosen as the lower end in determining the scope of the study. More than 1/3 (38%) of the students said they had casual sex, which increased significantly in students from 11 th and 12 th grade. Approximately half of the participants (42.10%) said that they were using protection during intercourse. Only 10.50% of all stated that they never used protection. The most commonly used protection was a condom combined with contraceptive drugs or intrauterine devices. Conclusion: The results show that young people are familiar with the basic methods of prevention, not only for pregnancy but also for STDs. The main sources of information for them are the Internet and conversations with parents.

Characterization of shaking intensity distribution and seismic assessment of RC buildings for the Kashmir (Pakistan) earthquake of October 2005

The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, called Mini-Clustering (MC), extends the partition-based approximation offered by mini-bucket elimination, to tree decompositions. The benefit of this extension is that all single-variable beliefs are computed (approximately) at once, using a two-phase message-passing process along the cluster tree. The resulting approximation scheme allows adjustable levels of accuracy and efficiency, in anytime style. Empirical evaluation against competing algorithms such as iterative belief propagation and Gibbs sampling demonstrates the potential of the MC approximation scheme for several classes of problems.

At present, in the mainstream sentiment analysis methods represented by Support Vector Machine, the vocabulary and the latent semantic information contained in the text cannot be well considered, and sentiment analysis of text is overly dependent on the statistics of sentiment words. In this paper, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived by the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information containing the probability features can be used as the classification features, the effect of classification for support vector machine can be improved, and the problem of not considering the latent semantic features in text sentiment analysis is solved. The results show that compared with the comparison method, the effect of the method proposed in this paper is obviously improved.

PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.

The aim of this paper is to categorize movies into genres using the previews. Our study attempts to combine audio, visual and text features to classify a collection of movie previews into action, biography, comedy, and horror. For each of the collected previews, the audio and visual features are extracted and the text features are drawn from social tags via social websites. The probabilistic latent semantic analysis (PLSA) is used to incorporate the features from these three different aspects of information. The standard PLSA processes one type of information only. Therefore double-model and triple-model PLSAs are extended in order to combine two or three different types of information. We compare these various variants of PLSA approaches with unimodal PLSAs, which use either audio, visual or text features only. The experimental results show not only that one of the triple-model PLSAs achieves the highest accuracy, but also that social tags (text features) play an important role for classifying movies genres.

Abstract: This study aimed to know The Effect of Using Cue Cards on Students’ Vocabulary Mastery at Seventh Grade Students of SMP N 16 Mandau. Related to the object of the research, the researcher used Pre - experimental method. The design of the research used pre test/ post test design. The research was conducted at Seventh Grade students of SMP N 16 Mandau. The population of this research was the seventh grade students of SMP N 16 Mandau. The sample of the research was 30 students. In analyzing the research data, the researcher used Paired sample T-Test by using SPSS. The data were taken from students’ English achievement pre test and post test score. Based on statistical calculation in data analysis, the researcher gave interpretation of pre test and post test score. From the calculation, t obtained was 10.343, the level significance of 5% was 2.05, and the level of significance 1% was 2.76. it showed 2.05>10.343>2.76. It could be concluded that alternative hypothesis (Ha) was accepted and the null hypothesis (Ho) was rejected. It means that there was significant effect of using cue cards on students’ vocabulary mastery at seventh grade students of SMP N 16 Mandau. Key words: Cue cards, Vocabulary mastery

This research aimed to analyzing student’s honest behavior grade VIIA and VIIB in SMP Negeri 6 Kota Jambi through integrated science learning in calor and its transfer. Data collection techniques are carried out by observation and documentation using questionnaire instruments. Data analysis in this research used descriptive statistical calculations. The result showed that student’s honest behavior grade VIIA in SMP Negeri 6 Kota Jambi through integrated science learning in calor and its transfer being in a good category, that is the percentage of 61,3%. While student’s honest behavior grade VIIB in SMP Negeri 6 Kota Jambi being in a very good category, that is the percentage of 60,0%. From all of result, it can be concluded that VIIA and VIIB grade student’s in SMP Negeri 6 Kota Jambi, have honest behavior in a very good category.

Endoscopy is an effective tool for variceal grading, but it is costly, invasive, and not always at hand. The study aimed to assess platelets count and ultrasonographic parameters of portal hypertension as predictors of esophageal grade. This, cross-sectional hospital based study conducted among100 patients with Schistosomal portal hypertension at Ibn Sina Teaching Hospital, Khartoum, Sudan during the period from November 2011 – August 2012. Patients with liver cirrhosis, previous treatment for esophageal varices or on β blockers were not included. Information collected were: socio-demographic data, history of and onset of variceal bleeding, ascites, SchistosomaMansoni Score (SMS), the spleen size, liver span, portal and splenic veins diameter, variceal grade, and platelets count. The ethical committee of the Sudan Medical Specialization Board approved the research and the Statistical Package for Social Sciences (SPSS, version 20, New York) was used for data analysis. Participants (76% males), Mean age (43.9 ± 14 years), liver span, spleen size below the costal margin, and portal vein diameter were, 10.34± 2.01, 6 ± 3.9, and 1.5 cm ± 0.27 cm respectively. Platelets count, SMS, the extent of periportal fibrosis, and portal vein diameter were significant predictors of esophageal grade,(P-value <0.05), while spleen size, portal vein diameter, platelets/spleen index were not. The study concluded that, non-invasive assessment of ultrasonographic parameters (ShistosomaMansoni Score, portal vein diameter, degree of periportal fibrosis) and platelets count could aid the detection of patients with large esophageal varies for the implementation of the needed intervention. The limitation was the small sample size.