Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)
Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …
that makes machine learning algorithms incompetent. Our study conceptually and …
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …
smart monitoring and decision-making solutions. Near real-time and online damage …
Personalized cross-silo federated learning on non-iid data
Non-IID data present a tough challenge for federated learning. In this paper, we explore a
novel idea of facilitating pairwise collaborations between clients with similar data. We …
novel idea of facilitating pairwise collaborations between clients with similar data. We …
A review of principal component analysis algorithm for dimensionality reduction
BMS Hasan, AM Abdulazeez - Journal of Soft Computing …, 2021 - publisher.uthm.edu.my
Big databases are increasingly widespread and are therefore hard to understand, in
exploratory biomedicine science, big data in health research is highly exciting because data …
exploratory biomedicine science, big data in health research is highly exciting because data …
A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction
Due to sharp increases in data dimensions, working on every data mining or machine
learning (ML) task requires more efficient techniques to get the desired results. Therefore, in …
learning (ML) task requires more efficient techniques to get the desired results. Therefore, in …
[HTML][HTML] A comprehensive survey of anomaly detection techniques for high dimensional big data
Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …
that has various applications in the real world. However, many existing anomaly detection …
Overview and comparative study of dimensionality reduction techniques for high dimensional data
S Ayesha, MK Hanif, R Talib - Information Fusion, 2020 - Elsevier
The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
[HTML][HTML] A review of physics-based machine learning in civil engineering
The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …
opportunities in all the sectors. ML is a significant tool that can be applied across many …
[HTML][HTML] State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
[HTML][HTML] TweepFake: About detecting deepfake tweets
The recent advances in language modeling significantly improved the generative
capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language …
capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language …