Comparison Of Dimensionality Reduction Techniques Research Articles

Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis

We have used a Ligand Knowledge Base for bidentate P,P-donor ligands of potential interest to homogeneous catalysis to compare three dimensionality reduction techniques, namely Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE). While our previous work on Ligand Knowledge Bases has focused on PCA, here we compare this approach with more recently-published approaches and assess the information retention, visualization, clustering and interpretability which can be achieved for each approach. We find that potential advantages of t-SNE are not realized with a database of the current size (275 entries), and that there is a degree of complementarity between PCA and UMAP. The statistics underlying PCA rely on linear relationships, making interpretation of the resulting plots comparatively straightforward. Since much of chemistry relies on linear structure-property relationships and low-dimensional visualization, the explainability and information retention achieved is attractive. UMAP proved more challenging to interpret, but achieved clear clustering which was often chemically meaningful, and it would be a useful approach for ensuring that distinct subsets of compounds are sampled in a machine-learning context. This analysis also highlighted that the tunability of catalysis achieved through ligand exchange maps well onto some areas of chemical space where closely related ligands cluster, while others represent outliers; these arise from different combinations of steric and electronic effects which chemists will find intuitive.

Comparison of dimensionality reduction techniques for multi-variable spatiotemporal flow fields

In the field of fluid mechanics, it is a potential consensus that nonlinear dimensionality reduction (DR) techniques outperform linear methods. However, this conclusion has been obtained based on simple fluid phenomena and an incomplete evaluation system of dimensionality reduction algorithms. In this study, we use an improved evaluation system of DR methods to compare and evaluate the performance of four DR methods, including two linear techniques: Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and two non-linear techniques: Isometric Mapping (ISOMAP) and Locally Linear Embedding (LLE). The four methods are applied to analyze a complex hydrodynamic flow field with cavitation by considering the joint features of multiple variables. Results show that LLE can capture redundant features that do not contribute to understanding the characteristics of flow fields, while ISOMAP is more suitable for handling datasets with multiple scales than LLE. PCA and ISOMAP can successfully capture the characteristics and evolution of flow fields. In addition, 3D supplementary information can assist ICA in improving the problem of identifying unstable flow field states.

Comparison of dimensionality reduction techniques for cross-source transfer of fluorescence contaminant detection models

Fluorescence spectroscopy shows promise as a tool for monitoring water quality due to its real-time capabilities and sensitive detection of several compounds of interest. Previous work has shown the possible use of fluorescence to detect and quantify low levels of polycyclic aromatic hydrocarbons and fluorescing pesticides. However, the fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources to build and calibrate them for each source water of interest. In this study, the novel application of data processing techniques was investigated to enable the transfer of fluorescence detection models from one water source to another. A contaminant detection model from a relatively consistent and low organic background source (Lake Ontario, TOC: 2.07–2.26 mg L−1) was transferred to the Otonabee River, which has higher organic concentrations and distinct characteristics (TOC: 5.20–5.66 mg L−1). Only a few additional fluorescence spectra of the background water quality and contaminants of interest were required to successfully transfer the model, without the need for labelled samples in the new source. Notable differences in peak location and spectral shape of identical compounds were found in source-specific models between the two water sources, implying variability in fluorescence signals resulting from environmental conditions. Despite the impact of environmental conditions, features identified by principal component analysis (PCA) and an autoencoder produced sensitive transferred models capable of addressing the spatial and temporal source diversity with mean absolute error (MAE) < 0.5 μg L−1 for quantification of PAHs and pesticides at concentrations between 0.1 and 7 μg L−1. The results of this study show the potential of the cross-source transferred model to be implemented in a wide range of environmental conditions.

Unsupervised Adaptation for High-Dimensional with Limited-Sample Data Classification Using Variational Autoencoder

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis.

Evaluation of Dimensionality Reduction Techniques for Load Profiling Application in Smart Grid Environment

The traditional power grid system is evolving toward a smart grid system, which will improve the user experience. However, this system is capable of generating high-dimensional data at a very high sample rate. A common technique for reducing high-dimensional data is dimensionality reduction. With this technique, we are able to reduce the data to a lower dimension, making it suitable for smart grid applications including transmission, storage, and visualization. Linear dimensionality reduction techniques are mostly explored in the context of smart grid applications. Due to the nonlinear nature of data generation in the smart grid, we anticipate that the nonlinear dimensionality reduction techniques can perform better. This work evaluates different nonlinear dimensionality reduction techniques and compares them with principal component analysis, which is a widely used linear dimensionality reduction technique in the smart grid environment. We use the visualization of load profile data and adjusted rank index (ARI) for comparison of dimensionality reduction techniques. Load profiling is an important task to complement the demand-side management and tariff selection. The visual depiction of the load profiles and ARI suggest that the nonlinear dimensionality reduction techniques perform better compared with linear dimensionality reduction techniques.

Accuracy comparison of dimensionality reduction techniques to determine significant features from IMU sensor-based data to diagnose vestibular system disorders

This study is a significant step gone to develop Machine Learning (ML) algorithm to apply to gait sensory information collected from people to identify Vestibular System (VS) disorders. Although ML is widely used as diagnostic tool in medical decision-making, there is not much research done on application of ML methods to identify VS imperfections. In this paper, we compared the accuracies of two dimensionality-reduction techniques to use with SVM with Gaussian Kernel: Feature Selection (FS) and Feature Transformation (FT) methods. T-test and Sequential Backward Selection (SBS) were used for FS and Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) with polynomial and Gaussian kernels were used as FT method. Both methods were applied to the dataset formed by 22 features collected from 37 people, of whom 21 were healthy and 16 subjects had VS-disorders. The highest accuracy among FT methods was 89.2%, while it was 81.1% for FS method. SVM with Gaussian Kernel, trained with the dataset of reduced dimensionality, had computation time of few hundreds of milliseconds, which makes real-time data processing possible. The importance of this work will obviously increase with the increase in the number of initial features. As a next step, we aim to increase dataset and use additional features extracted from pressure sensors placed under the feet. We also aim to use time domain characteristics of the features to increase overall accuracy as a next step.

Efficient Information Retrieval through Comparison of Dimensionality Reduction Techniques with Clustering Approach

In day today life huge amount of electronic data is generated from various resources. Such data is literally large and not easy to work with for storage and retrieval. This type of data can be treated with various efficient techniques for cleaning, compression and sorting of data. Preprocessing can be used to remove basic English stop-words from data making it compact and easy for further processing; later dimensionality reduction techniques make data more efficient and specific. This data later can be clustered for better information retrieval. This paper elaborates the various dimensionality reduction and clustering techniques applied on sample dataset C50test of 2500 documents giving promising results, their comparison and better approach for relevant information retrieval.

Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals

Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination.

Radial basis function neural network based comparison of dimensionality reduction techniques for effective bearing diagnostics

This article uses the cluster dependent weighted fuzzy C-means based radial basis function neural network for comparing the different dimensionality reduction techniques for the fault diagnosis in the rolling element bearing. The vibration signals from normal bearing, bearing with defect on the inner race, and bearing with defect on the outer race were acquired under one radial load and two shaft speeds. These signals were subjected to the wavelet transform based denoising from which several time and frequency domain features were extracted. Dimensionality reduction techniques, namely, principal component analysis, Fisher’s criterion, and separation index, have been used to select the sensitive features. The selected features were used to train and test the radial basis function neural network, where the centers of the radial basis function units have been optimized by the cluster dependent weighted fuzzy C-means and the widths of the radial basis function units have been fixed by trial and error. Finally, a comparison of the dimensionality reduction techniques based on the radial basis function neural network performance is presented.