Data analysis using principal component analysis

Sehgal, Shruti; Singh, Harpreet; Agarwal, Mohit; Bhasker, V.; Shantanu, _

doi:10.1109/medcom.2014.7005973

Cited by 63 publications

(21 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8, the daily contribution of each sample was obtained by equation. (35), and presented periodic variations and differences between different months.…”

Section: ) Daily Sample Contributionmentioning

confidence: 99%

“…Information compression is designed to reduce training time by filtering noise and redundant data to compress data sets. The representative algorithms of information compression are the condensed nearest neighbor (CNN) rule [32], [33], and principal component analysis (PCA) [34], [35], among others [36]. Nikolaidis et al [37] introduced a multistage method for pruning the training set, which can much improve storage reduction and competitive execution speeds.…”

Section: B the Sample Selection Workmentioning

confidence: 99%

“…where the t is the number of all days in one week in all sample months. c ri is calculated by equation (35).…”

Section: Optimization Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP Algorithm

Pei-kun

2020

IEEE Access

View full text Add to dashboard Cite

Due to the wide applications of deep learning in the field of urban rail transit passenger flow forecasting, the selection problem of training samples has become increasingly more worthy of researchers' attention, as it is closely related to urban rail transit passenger flow time series. Therefore, it is necessary to study the distribution characteristics of the contribution degree of the training sample to guide sample selection in the deep learning training process. In this study, based on the prediction accuracy and the sample contribution degree, the optimal sample contribution combination algorithm (GWO-SCBP) was ultimately generated by the grey wolf optimizer (GWO) and error back propagation (EBP) algorithms. The contribution of training samples for each station of the Xi'an metro network was calculated and analyzed. The results show that the sample contribution is not only related to the distance between the sample and predicted value, but is also closely related to the station flow characteristics. By classifying the network stations and fitting the contribution degree of the central station of each type of station, linear equations of sample contribution degree were obtained, and the R 2 values attained at least 0.65, indicating a good fitting effect. INDEX TERMS Rail transit, passenger flow forecast, sample selection, grey wolf optimizer algorithm, artificial neural network.

show abstract

“…8, the daily contribution of each sample was obtained by equation. (35), and presented periodic variations and differences between different months.…”

Section: ) Daily Sample Contributionmentioning

confidence: 99%

Section: B the Sample Selection Workmentioning

confidence: 99%

See 1 more Smart Citation

Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP Algorithm

Pei-kun

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…We found that methanol fixation does not dramatically change single-cell RNA transcriptomic profiles, but scRNA-seq is most commonly used to perform cell-type identification and clustering, therefore we further explored our data using classification methods to ensure fixation does not affect these types of analyses and downstream biological inferences. Principal component analysis (PCA) is a commonly used technique in single-cell RNA-seq analysis (26). It identifies the coordinate system that represents the greatest variance in the data, and projecting data points in this new coordinate system, thus is able to visualize the differences between groups of data points and cluster similar data points together.…”

Section: Methanol Fixation Does Not Affect Cell-type Identification mentioning

confidence: 99%

Effect of methanol fixation on single-cell RNA sequencing data

Wang

Liu

2020

Preprint

View full text Add to dashboard Cite

Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-seq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

show abstract

“…Burges and Christopher (2010) presented a guided tour in machine learning approach to reduce the dimensionality of records. PCA (Jolliffe, 1986;Sehgal et al, 2014) is one of the most accepted techniques for dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Missing Values Treatment and Feature Reduction Analysis to Enhance Classification

Muralidharan

Renuka

Jaswant

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

Datasets may have large number of features which makes it hard and time consuming to classify. Additionally, they may have irrelevant and noise features too with missing values. The missing values should be treated in a proper way so that the classifier accuracy can be improved. There is also a need to reduce features and select only the features necessary to the classifier. Principal Component Analysis (PCA) is commonly considered for this process of reducing the number of features in a dataset. These reduced components can be applied as input to the classifiers. In this study, standard datasets are checked for missing values, classified using Support vector Machines (SVM) and Naive Bayes with and without reducing the features using PCA. Then, the proposed algorithm for missing value imputation is used on the datasets and the same analysis were carried out. The accuracy is evaluated using Confusion Matrix. The results are discussed with analysis based on the nature of features and missing values and how different datasets behave when used with machine learning algorithms.

show abstract

Data analysis using principal component analysis

Cited by 63 publications

References 5 publications

Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP Algorithm

Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP Algorithm

Effect of methanol fixation on single-cell RNA sequencing data

Missing Values Treatment and Feature Reduction Analysis to Enhance Classification

Contact Info

Product

Resources

About