A survey on Laplacian eigenmaps based manifold learning methods

Li, Bo; Li, Yan-Rui; Zhang, Xiaolong

doi:10.1016/j.neucom.2018.06.077

Cited by 49 publications

(16 citation statements)

References 127 publications

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“…Laplacian Eigenmaps [11] (LE) has remarkable properties of preserving local neighborhood structure of data. LE is to construct the relationship between data with local angles and reconstruct the local structure and features of the data by constructing adjacency graph [30]. If two data instances x i and x j are very similar, i and j should be as close as possible in the target subspace after dimensionality reduction.…”

Section: Laplacian Eigenmapsmentioning

confidence: 99%

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Zhou

Shi

et al. 2021

Remote Sensing

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Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms—the reconstruction term and the embedding term of a specific task—is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods.

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Section: Laplacian Eigenmapsmentioning

confidence: 99%

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Zhou

Shi

et al. 2021

Remote Sensing

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“…To find projection of any additional samples, LE needs to be run on all the data together with the additional samples, resulting in considerable computational cost especially when applying it to large scale data pattern recognition. Fortunately, various methods have been developed to mitigate the out-of-sample problem [39]: Linear approximation to LE, Kernel extensions to LE,…”

Section: Out-of-sample Extensionmentioning

confidence: 99%

Electromyography Classification during Reach-to-Grasp Motion using Manifold Learning

Lashgari

Maoz

2020

Preprint

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Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for sensing muscle activity. However, EMG is also noisy, complex, and high-dimensional. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and in particular to measure reaching and grasping motions of the human hand. Here, we developd a more automated pipeline to predict object weight in a reach-and-grasp task from an open dataset relying only on EMG data. In that we shifted the focus from manual feature-engineering to automated feature-extraction by using raw (filtered) EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran some classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% for 3-way classification). Our results, using EMG alone, are comparable to others in the literature that used EMG and EEG together. They also demonstrate the usefulness of dimensionality reduction when classifying movement based on EMG signals and more generally the usefulness of EMG for movement classification.

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“…A selected subset of the first several PCs rather than the original spectral vectors is then used for classification; thus, the feature dimension can be significantly reduced. Besides, some manifold learning methods are further developed to analyze the intrinsic features of HSI, including the Laplacian eigenmaps (LEs) [14], locally linear embedding (LLE) [15], and its extension robust local manifold representation (RLMR) [16]. However, these methods only consider the spectral information, leading to some limitations due to a lack of spatial analysis.…”

Section: Introductionmentioning

confidence: 99%

SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial–Spectral Feature Extraction in Hyperspectral Image

Sun

Ren

et al. 2022

IEEE Trans. Cybern.

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A survey on Laplacian eigenmaps based manifold learning methods

Cited by 49 publications

References 127 publications

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Electromyography Classification during Reach-to-Grasp Motion using Manifold Learning

SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial–Spectral Feature Extraction in Hyperspectral Image

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