Random fields representation over manifolds via isometric feature mapping‐based dimension reduction

2021 ◽  
Author(s):  
De‐Cheng Feng ◽  
Yan‐Ping Liang ◽  
Xiaodan Ren ◽  
Jie Li
Keyword(s):  
Dimension Reduction ◽  
Random Fields ◽  
Feature Mapping ◽  
Isometric Feature Mapping

Face recognition based on improved isometric feature mapping algorithm

2013 ◽  
Vol 33 (1) ◽  
pp. 76-79
Author(s):  
Jiamin LIU ◽  
Huiyan WANG ◽  
Xiaoli ZHOU ◽  
Fulin LUO
Keyword(s):  
Face Recognition ◽  
Feature Mapping ◽  
Mapping Algorithm ◽  
Isometric Feature Mapping

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.

Quasi-classical trajectory analysis with isometric feature mapping and locally linear embedding: deep insights into the multichannel reaction on an NH3+(4A) potential energy surface

2020 ◽  
Vol 22 (31) ◽  
pp. 17460-17471 ◽  
Author(s):  
Weiliang Shi ◽  
Tian Jia ◽  
Anyang Li
Keyword(s):  
Potential Energy ◽  
Trajectory Analysis ◽  
Energy Surface ◽  
Classical Trajectory ◽  
Locally Linear Embedding ◽  
Feature Mapping ◽  
Classical Trajectories ◽  
Linear Embedding ◽  
Isometric Feature Mapping ◽  
Locally Linear

Two manifold learning methods, isometric feature mapping and locally linear embedding, are applied to the analysis of quasi-classical trajectories for multi-channel reaction NH+ + H2 → N + H3+/NH2+ + H.

scholarly journals Comparación de métodos de reducción de dimensión basados en análisis por localidades

TecnoLógicas ◽  
2010 ◽  
pp. 131
Author(s):  
Juliana Valencia-Aguirre ◽  
Genaro Daza-Santacoloma ◽  
Carlos D. Acosta ◽  
Germán Castellanos-Domínguez
Keyword(s):  
Locally Linear Embedding ◽  
Feature Mapping ◽  
Maximum Variance ◽  
Linear Embedding ◽  
Isometric Feature Mapping ◽  
Maximum Variance Unfolding ◽  
Locally Linear

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

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