scholarly journals Large-scale manifold learning

2008 ◽  
Author(s):  
Ameet Talwalkar ◽  
Sanjiv Kumar ◽  
Henry Rowley
Keyword(s):  
Manifold Learning ◽  
Large Scale

scholarly journals Prediction of hot spots in protein–DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Ke Li ◽  
Sijia Zhang ◽  
Di Yan ◽  
Yannan Bin ◽  
Junfeng Xia
Keyword(s):  
Feature Selection ◽  
Manifold Learning ◽  
Hot Spots ◽  
Large Scale ◽  
Computational Method ◽  
Gradient Boosting ◽  
Feature Mapping ◽  
Accessible Information ◽  
Extreme Gradient Boosting ◽  
Isometric Feature Mapping

Abstract Background Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. Results Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. Conclusion Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

High-dimensional MRI data analysis using a large-scale manifold learning approach

2013 ◽  
Vol 24 (5) ◽  
pp. 995-1014 ◽  
Author(s):  
Loc Tran ◽  
Debrup Banerjee ◽  
Jihong Wang ◽  
Ashok J. Kumar ◽  
Frederic McKenzie ◽  
...  
Keyword(s):  
Data Analysis ◽  
Manifold Learning ◽  
Large Scale ◽  
High Dimensional ◽  
Learning Approach

scholarly journals Identifying multiscale spatio-temporal patterns in human mobility using manifold learning

2020 ◽  
Vol 6 ◽  
pp. e276 ◽  
Author(s):  
James R. Watson ◽  
Zach Gelbaum ◽  
Mathew Titus ◽  
Grant Zoch ◽  
David Wrathall
Keyword(s):  
Manifold Learning ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Network ◽  
Human Migration ◽  
Mobility Patterns ◽  
Great Opportunity ◽  
Spectral Graph ◽  
Spectral Graph Wavelets ◽  
Spatio Temporal

When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. To identify and characterize the main spatio-temporal scales and patterns of human mobility we examined CDR data from the Orange mobile network in Senegal using a new form of spectral graph wavelets, an approach from manifold learning. This unsupervised analysis reduces the dimensionality of the data to reveal seasonal changes in human mobility, as well as mobility patterns associated with large-scale but short-term religious events. The novel insight into human mobility patterns afforded by manifold learning methods like spectral graph wavelets have clear applications for urban planning, infrastructure design as well as hazard risk management, especially as climate change alters the biophysical landscape on which people work and live, leading to new patterns of human migration around the world.

Artificial Neural Network Based Intrusion Detection Method Combined with Manifold Learning Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 3170-3174
Author(s):  
Jin Guang Chen ◽  
Zhi Xiong Li
Keyword(s):  
Intrusion Detection ◽  
Manifold Learning ◽  
Detection Rate ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Space ◽  
Locally Linear Embedding ◽  
Ann Model ◽  
Network Intrusion ◽  
Artificial Neural

Computer and network security is one of the most emergency issues for a large scale of applications. The unexpected intrusion may make terrible disaster to the network users. It is therefore imperative to detect the network attacks to prevent this kind of violations. The intrusion patter recognition is now a hot topic in this research area. The use of the artificial neural networks (ANN) can provide intelligent intrusion detection. However, the intrusion detection rate is often affected by the input feature vector of the ANN. This is because the original feature space always contains a certain number of useless features. To overcome this problem, a new network intrusion detection approach based on manifold learning nonlinear feature dimension descending and ANN classifier is presented in this paper. The locally linear embedding (LLE) algorithm was used to reduce the original intrusion feature space. Then the satisfactory ANN model with proper input features was obtained. The efficiency of the proposed method was evaluated with the real intrusion data. The analysis results show that the proposed approach has good intrusion detection rate, and performs better than the standard GA-ANN method.

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