Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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Loss functions between PCA-DNN model and PCA-LSTM model. by Cihun-Siyong Alex Gong (11874066)

Average accuracy (across all 12 subject) as a function of the number of eigenvectors obtained with different dimensionality reduction techniques for the best classifier—k-NN (see a... by Elnaz Lashgari (11278808)

Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

Principal component analysis (PCA) of sub-layer mean intensities. by Maëlle Vilbert (17101954)

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

The loss function of network structures, LPC-DNN, LPC-PCA-DNN, LPC-LSTM, LPC-PCA-LSTM, MFCC-DNN, MFCC-PCA-DNN, MFCC-LSTM and MFCC-PCA-LSTM, as the epoch increases. by Cihun-Siyong Alex Gong (11874066)

Results obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Convergence curves obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Different algorithms in 23 benchmark functions to obtain partial boxplot. by Yuqi Xiong (12343771)

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Results obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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Algorithm. by Daniel Sanchez-Taltavull (11654630)

Average function evaluation times of the three optimization algorithms. by Shuhui Yu (1535500)

Convergence curves obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

Functions of genes, detected simultaneously in 3 "diseased" blocks («Prostate Disease», «PCa», «Not PCa») and missing in the "Healthy" block and one gene (HRAS), found only in the... by O. E. Bryzgunova (6577043)

PCA calculation FRHGs score. by Lixia Chen (614030)

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

Genes that contribute to PCA and GO terms of enriched and differentially expressed genes. GO, gene ontology; PCA, principal component analysis. by Tomokatsu Udagawa (11675636)

Functional PCA With Covariate-Dependent Mean and Covariance Structure by Fei Ding (134577)

“…<p>Incorporating covariates into functional principal component analysis (PCA) can substantially improve the representation efficiency of the principal components and predictive performance. …”