AIMC Topic: Principal Component Analysis

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Machine learning of pair-contact process with diffusion.

Scientific reports
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified...

Unsupervised classification of voltammetric data beyond principal component analysis.

Chemical communications (Cambridge, England)
In this study, we evaluate different apoproaches to unsupervised classification of cyclic voltammetric data, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projec...

Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioec...

Optimization and Application of Information Visualization Design Based on Image Symbol under the Guidance of Feature Integration Theory.

Contrast media & molecular imaging
Increasingly, today's businesses rely on data visualization to aid in the outcome that is directly linked to the bulk of their earnings. Due to the enormous volume, speed, and accuracy requirements of data management, database professionals are becom...

Haplotype and population structure inference using neural networks in whole-genome sequencing data.

Genome research
Accurate inference of population structure is important in many studies of population genetics. Here we present HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phas...

Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors.

Sensors (Basel, Switzerland)
Failure mode detection is essential for bearing life prediction to protect the shafts on the machinery. This work demonstrates the rolling bearing vibration measurement, signals converting and analysis, feature extraction, and machine learning with n...

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients.

Computational intelligence and neuroscience
The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV t...

Analysis and Prediction of Corporate Finance and Exchange Rate Correlation Based on Machine Learning Algorithms.

Computational intelligence and neuroscience
Based on the risk management of exposure to foreign exchange assets and liabilities and the application of financial derivatives, this paper provides an in-depth analysis of the financial and exchange rate risks of foreign-funded enterprises. Therefo...

A Computational Intelligence Model for Legal Prediction and Decision Support.

Computational intelligence and neuroscience
Legal judgment prediction (LJP) and decision support aim to enable machines to predict the verdict of legal cases after reading the description of facts, which is an application of artificial intelligence in the legal field. This paper proposes a leg...

Autoencoders reloaded.

Biological cybernetics
In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value deco...