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Attention-Aware Residual Network Based Manifold Learning for White Blood Cells Classification
IEEE journal of biomedical and health informatics, 2020The classification of six types of white blood cells (WBCs) is considered essential for leukemia diagnosis, while the classification is labor-intensive and strict with the clinical experience.
Pu Huang +9 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang, Sridhar Mahadevan
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Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang, Sridhar Mahadevan
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2017
Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies.
Felix J S, Bragman +4 more
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Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients with discordant effects on lung physiology. This limits the explanatory power of mean values in clinical studies.
Felix J S, Bragman +4 more
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Statistical Learning via Manifold Learning
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015A new geometrically motivated method is proposed for solving the non-linear regression task consisting in constructing a predictive function which estimates an unknown smooth mapping f from q-dimensional inputs to m-dimensional outputs based on a given 'input-output' training pairs. The unknown mapping f determines q-dimensional Regression manifold M(f)
Alexander Bernstein +2 more
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2012
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-
Mateus, Diana +4 more
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Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-
Mateus, Diana +4 more
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Continuum Isomap for manifold learnings
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zha, Hongyuan, Zhang, Zhenyue
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Manifold-Based Learning and Synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009This paper proposes a new approach to analyze high-dimensional data set using low-dimensional manifold. This manifold-based approach provides a unified formulation for both learning from and synthesis back to the input space. The manifold learning method desires to solve two problems in many existing algorithms.
Dong, Huang, Zhang, Yi, Xiaorong, Pu
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MANIFOLD LEARNING FOR ROBOT NAVIGATION
International Journal of Neural Systems, 2006In this paper we introduce methods to build a SOM that can be used as an isometric map for mobile robots. That is, given a dataset of sensor readings collected at points uniformly distributed with respect to the ground, we wish to build a SOM whose neurons (prototype vectors in sensor space) correspond to points uniformly distributed on the ground ...
Keeratipranon, Narongdech +2 more
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SPD Manifold Deep Metric Learning for Image Set Classification
IEEE Transactions on Neural Networks and Learning SystemsBy characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress.
Rui Wang +4 more
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CFRP Impact Damage Inspection Based on Manifold Learning Using Ultrasonic Induced Thermography
IEEE Transactions on Industrial Informatics, 2019Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods.
Xiaofei Zhang +6 more
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