Results 1 to 10 of about 26,804 (294)
Haisu: Hierarchically supervised nonlinear dimensionality reduction. [PDF]
PLoS Computational Biology, 2022 We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the ...
Kevin Christopher VanHorn +1 more
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On nonlinear dimensionality reduction for face recognition [PDF]
Image and Vision Computing, 2012 The curse of dimensionality has prompted intensive research in effective methods of mapping high dimensional data. Dimensionality reduction and subspace learning have been studied extensively and widely applied to feature extraction and pattern representation in image and vision applications.
Hujun Yin
exaly +6 more sources
A biological model of nonlinear dimensionality reduction. [PDF]
Sci Adv, 2023 Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) have been developed, their implementation in
Yoshida K, Toyoizumi T.
europepmc +3 more sources
Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach [PDF]
Sensors, 2019 Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time ...
Marek Piorecky +4 more
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Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction. [PDF]
PLoS BiologySpike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in ...
Daniel Suárez-Barrera +11 more
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Distortion-Free Nonlinear Dimensionality Reduction [PDF]
Lecture Notes in Computer Science, 2008 Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method ...
Changshui Zhang, Zhang Changshui
exaly +2 more sources
Remote Sensing, 2020 Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract ...
Yongguang Zhai +4 more
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Nonlinear dimensionality reduction for clustering [PDF]
Pattern Recognition, 2020 Abstract We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters.
Sotiris K. Tasoulis +2 more
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Nonlinear dimensionality reduction on graphs [PDF]
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while
Yanning Shen +2 more
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IEEE Access, 2021 Dimensionality reduction is a hot research topic in pattern recognition. Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.
Shuzhi Su, Gang Zhu, Yanmin Zhu
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