Adaptive dimensionality reduction for neural network-based online principal component analysis

Abstract

“Principal Component Analysis” (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. Therefore, existing approaches cannot account for abrupt changes in the presented data. The contribution of this work is to enable in neural network-based PCA the continuous dimensionality adjustment by an arbitrary number without the necessity to learn all principal components. A novel algorithm is presented that utilizes several PCA characteristics to adaptivly update the optimal number of principal components for neural network-based PCA. A precise estimation of the required dimensionality reduces the computational effort while ensuring that the desired amount of variance is kept. The computational complexity of the proposed algorithm is investigated and it is benchmarked in an experimental study against other neural network-based and incremental PCA approaches where it produces highly competitive results.

Intellectual Data Aggregation Using Independent Cluster based Medicaid Method for Network Functionality Fabrication

“…The proposed method investigates the clusters with the performance metrics given in the equations 5 to 7, Comparing with the existing methods which are discussed in the review literature. (2) 3 75.34 84.24 83.56 DBSCAN (5) 5,7 83.15 89.00 90.31 GSVM (6) 5 79.76 90.45 92.22 SOM (8) 5 75.00 78.73 93.51 PCA (11) 3, 5 85.69 94.88 93.11 ocSVM (15) 3, 5 80.23 92.57 90.55 ICA (20) 3 From Table 3, K-Means process the three different cluster classes to find the accuracy level to 83.56%, the energy consumption rate is low to compute the cluster set. But the Algorithm improves the classification to 84.24% comparing the other network functionality to fabricate the attacking nodes.…”

“…Migenda N et al (11) have suggested dimensionality lessening methods like PCA with numerical controller charts for guided wave-based unsupervised damage assessment. This method makes use of PCA as a feature separator to decrease the dimensionality of the input space.…”

A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces

“…Spearman correlation was measured between independent variables. Another method of applying explanatory statistics included PCA to orthogonalize the high-dimensionality original dataset into useful latent hits with eigenvalues equal to 1 or greater by KMO test [36]. All analyses were carried out in the environment of R-project for statistical computing and graphics [37].…”

A synergistic bacterial pool decomposes tebuthiuron in soil

“…A box-plot diagram was elaborated to describe the radicle-to-hypocotyl ratio for samples from ecotoxicological bioassay, separating them using the post-hoc Tukey’s test. Principal component analysis 16 was conducted to extract functional relationships between respirometry and ecotoxicology. All analyses were performed in the environment of the software R-project for statistical computing and graphics 17 .…”