Application Of Dimensionality Reduction Methods Research Articles

Mass Spectrometry Imaging Combined with Sparse Autoencoder Method Reveals Altered Phosphorylcholine Distribution in Imipramine Treated Wild-Type Mice Brains.

Mass spectrometry imaging (MSI) is essential for visualizing drug distribution, metabolites, and significant biomolecules in pharmacokinetic studies. This study mainly focuses on imipramine, a tricyclic antidepressant that affects endogenous metabolite concentrations. The aim was to use atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI)-MSI combined with different dimensionality reduction methods to examine the distribution and impact of imipramine on endogenous metabolites in the brains of treated wild-type mice. Brain sections from both control and imipramine-treated mice underwent AP-MALDI-MSI. Dimensionality reduction methods, including principal component analysis, multivariate curve resolution, and sparse autoencoder (SAE), were employed to extract valuable information from the MSI data. Only the SAE method identified phosphorylcholine (ChoP) as a potential marker distinguishing between the control and treated mice brains. Additionally, a significant decrease in ChoP accumulation was observed in the cerebellum, hypothalamus, thalamus, midbrain, caudate putamen, and striatum ventral regions of the treated mice brains. The application of dimensionality reduction methods, particularly the SAE method, to the AP-MALDI-MSI data is a novel approach for peak selection in AP-MALDI-MSI data analysis. This study revealed a significant decrease in ChoP in imipramine-treated mice brains.

Optimizing Mental Health Detection in Indian Armed Forces Personnel through Feature Engineering Driven Dataset Reduction, Addressing Suicide, Depression, and Stress

Within the realm of machine learning, the construction of high-quality datasets stands as a crucial factor profoundly influencing model performance. This research aims to furnish a comprehensive guide for enhancing the accuracy and efficiency of dataset construction. It achieves this by integrating multi-variate reduction techniques and innovative feature engineering strategies, implemented within the Python programming ecosystem. As the landscape of datasets becomes increasingly diverse and complex, the imperative to optimize precision grows more critical. This study explores the judicious application of dimensionality reduction methods, such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), alongside various feature selection approaches to strategically streamline datasets while preserving vital information. In conjunction with these reduction techniques, the research introduces novel feature engineering methods to amplify the discriminative power of remaining features, thereby enriching the dataset's representational capacity. The exploration spans a spectrum of multi-variate reduction techniques and delves into feature engineering methodologies, including polynomial feature creation, interaction term generation, and domain-specific transformation functions. Practical implementations of these techniques are demonstrated through Python, showcasing their applicability across diverse domains. Empirical evaluations on real-world datasets underscore the efficacy of the proposed methodology, revealing superior accuracy and efficiency compared to conventional dataset construction approaches. The insights derived from this research contribute significantly to the broader discourse in machine learning, presenting a generic yet potent framework for enhancing precision in datasets. Beyond deepening our understanding of multi-variate reduction and feature engineering, the findings offer a practical guide for researchers and practitioners seeking to optimize precision in various machine learning applications.

Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study

Hierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reduction can be performed by feature extraction or feature selection, according to the problem domain and datasets characteristics. This work carried out a systematic literature mapping to identify the approaches and techniques of dimensionality reduction that have been used in hierarchical multi-label classification tasks. Searches were performed on 7 important databases for the Computer Science field. From a list of 184 retrieved papers, 12 were selected for analysis, from which it was possible to determine a general overview of studies conducted from 2010 to 2022. It was identified that feature selection was the most frequent reduction method, with filter approach standing out. In addition, it was detected that most of the works used tree hierarchical structure. As its main outcome, this paper presents the state of the art of dimensionality reduction problem for hierarchical multi-label classification, indicating trends and research issues in the field.

Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder

Estimating rock and fluid properties in the subsurface from geophysical measurements is a computationally and memory-intensive inverse problem. For nonlinear problems with non-Gaussian variables, analytical solutions are generally not available, and the solutions of those inverse problems must be approximated using sampling and optimization methods. To reduce the computational cost, model and data can be reparameterized into low-dimensional spaces where the solution of the inverse problem can be computed more efficiently. Among the potential dimensionality reduction methods, deep-learning algorithms based on deep generative models provide an efficient approach to reduce the dimension of the model and data vectors. However, such dimension reduction might lead to information loss in the reconstructed model and data, reduction of the accuracy and resolution of the inverted models, and under- or overestimation of the uncertainty of the predicted models. To comprehensively investigate the impact of model and data dimension reduction with deep generative models on uncertainty quantification, we compare the prediction uncertainty in nonlinear inverse problem solutions obtained from Markov chain Monte Carlo and ensemble-based data assimilation methods implemented in lower dimensional data and model spaces using a deep variational autoencoder. Our workflow is applied to two geophysical inverse problems for the prediction of reservoir properties: prestack seismic inversion and seismic history matching. The inversion results consist of the most likely model and a set of realizations of the variables of interest. The application of dimensionality reduction methods makes the stochastic inversion more efficient.

People Walking Classification Using Automotive Radar

Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences.

Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.

Study on the application of dimensionality reduction method on reliability and reliability sensitivity analysis of random vibration systems

The application of dimensionality reduction method on the reliability and reliability sensitivity analysis of vibration systems was examined. The dimensionality of a vibration reliability problem was reduced to only two independent dimensions by the means of polar transformation. As the safe and failure classes of samples are clearly distinguishable and occupy a standard position in a two-dimensional plot in the case that the important direction exists, the relevant samples are selected visually. The reliability and reliability sensitivity problems were solved using the position of the relevant samples. Before the reliability and reliability sensitivity estimation, an essential visualization analysis is conducted to examine the capacity of the method in terms of the existence of the important direction which is used to reduce the dimensionality of the reliability problem. In order to calculate the design point correctly, the improved Hasofer–Lind–Rackwitz–Fiessler method was extended with a procedure for determining the perturbation in the calculation of the gradient vector by finite differences according to the numerical precision of the limit state function. The dimensionality reduction method saves the numbers of calling limit state function a lot and has the same accuracy as Monte Carlo method. Examples involving single and multiple degree-of-freedom nonlinear vibration systems were used to demonstrate the approach.