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Semi-supervised Neighborhood Preserving Discriminant Embedding: A Semi-supervised Subspace Learning Algorithm
[chapter]
2011
Lecture Notes in Computer Science
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In this paper, we propose a subspace learning method based on semi-supervised neighborhood preserving discriminant learning, which we call Semi-supervised Neighborhood Preserving Discriminant Embedding ...
Over the last decade, supervised and unsupervised subspace learning methods, such as LDA and NPE, have been applied for face recognition. ...
Conclusion In this paper, we propose a new linear subspace learning algorithm called Semi-supervised Neighborhood Discriminant Embedding. ...
doi:10.1007/978-3-642-19318-7_16
fatcat:zwn2d7vrunhitel27uzwkasxmi
Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor
[chapter]
2012
Principal Component Analysis
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With the local descriptor tensor of image representation, we propose to use a tensor subspace analysis algorithm, which is called as multilinear Supervised Neighborhood Preserving Embedding (MSNPE), for ...
As we know, subspace learning approaches, such as PCA and LDA by Belhumeur et al. (1997) , have widely used in computer vision research filed for feature extraction or selection and have been proven to ...
conventional subspace analysis methods.
105 Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor www.intechopen.com ; N:s a m p l en u m b e r ; n: dimension ...
doi:10.5772/37457
fatcat:3icdyoecb5ajznn5e3jbi7m6pe
Subspace Regularization: A New Semi-supervised Learning Method
[chapter]
2009
Lecture Notes in Computer Science
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Based on this, we formulate the problem of semi-supervised learning as a task of finding a subspace and a decision function on the subspace such that the projected data are well separated and the original ...
To overcome this problems, we introduce into semi-supervised learning the classic low-dimensionality embedding assumption, stating that most geometric information of high dimensional data is embedded in ...
In this paper, we turn to consider the low-dimensionality embedding assumption in the semi-supervised learning setting. ...
doi:10.1007/978-3-642-04174-7_38
fatcat:tjlv23hno5htrn2i2lku374dau
A General Approach for Achieving Supervised Subspace Learning in Sparse Representation
2019
IEEE Access
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Moreover, by utilizing the proposed approach, we achieve a new supervised subspace learning algorithm named supervised principal coefficients embedding (SPCE). ...
In this paper, we propose an approach which can be used as a general way for developing a corresponding supervised algorithm based on any unsupervised subspace learning algorithm using sparse representation ...
In fact, some algorithms such as Sparse Distance Preserving Embedding (SDPE) and Sparse Proximity Preserving Embedding [29] indeed propose a way to extend the unsupervised subspace learning algorithm ...
doi:10.1109/access.2019.2898923
fatcat:teu4jcdqs5anbcscwlbhaykgdu
Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method
2019
Sensors
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This study has been conducted to develop a new non-linear subspace learning method named "supervised kernel locality-based discriminant neighborhood embedding," which performs data classification by learning ...
an optimum embedded subspace from a principal high dimensional space. ...
In order to address these problems, we have proposed a new supervised subspace learning algorithm named "supervised kernel locality-based discriminant neighborhood embedding" (SKLDNE). ...
doi:10.3390/s19071643
fatcat:uwjitlzxy5gspdnv5nesgysdau
Semi-Supervised Classification Based on Mixture Graph
2015
Algorithms
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SSCMG first constructs multiple k nearest neighborhood (kNN) graphs in different random subspaces of the samples. ...
SSCMG can preserve the local structure of samples in subspaces and is less affected by noisy and redundant features. ...
[10] suggested a compact graph based semi-supervised learning (CGSSL) method for image annotation. ...
doi:10.3390/a8041021
fatcat:2r2pc5l2njgrvm6dkwhnt47bwi
Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear Manifold Clustering Algorithms
[article]
2021
arXiv
pre-print
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The majority of the prominent subspace clustering algorithms rely on the representation of the data points as linear combinations of other data points, which is known as a self-expressive representation ...
We introduce a new taxonomy to classify the state-of-the-art approaches into three categories, namely locality preserving, kernel based, and neural network based. ...
Self-supervised subspace clustering The success of supervised feature learning in neural networks inspired a few works to map the unsupervised SC task to a (self-)supervised problem. ...
arXiv:2103.10656v1
fatcat:vtlb3d337fgixkumu5faidisrm
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2006.02330v1 fatcat:z7keyuqq6zhtbcqle2tvuut7jq
In this work, we first present a theoretical analysis of learning multi-modal nonlinear embeddings in a supervised setting. ...
We then propose a multi-modal nonlinear representation learning algorithm that is motivated by these theoretical findings, where the embeddings of the training samples are optimized jointly with the Lipschitz ...
Our next contribution is to propose a new supervised nonlinear multi-modal learning algorithm. ...
arXiv:2006.02330v2
fatcat:iavrcnvk5baynazymzfaru75aa
A novel supervised feature extraction and classification fusion algorithm for land cover recognition of the off-land scenario
2014
Neurocomputing
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In this paper, a novel supervised feature extraction and classification fusion algorithm based on neighborhood preserving embedding (NPE) and sparse representation is proposed. ...
Specifically, an optimal dictionary is adaptively learned to bate the trivial information of the original training data; then, in order to obtain the sparse representation coefficients, a sparse preserving ...
The neighborhood preserving embedding NPE is an unsupervised manifold learning algorithm that computes low-dimensional, neighbor-hood-preserving embedding of high-dimensional inputs. ...
doi:10.1016/j.neucom.2014.03.034
fatcat:6cpkkozxa5bw3bv4ulqdrzflp4
Semi-Supervised Classification Based on Low Rank Representation
2016
Algorithms
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Recently, low-rank representation has been introduced to construct a graph, which can preserve the global structure of high-dimensional samples and help to train accurate transductive classifiers. ...
Then, the coefficient matrix is adopted to define a graph. Finally, SSC-LRR incorporates this graph into a graph-based semi-supervised linear classifier to classify unlabeled samples. ...
[9] proposed a method called compact graph based semi-supervised learning (CGSSL). ...
doi:10.3390/a9030048
fatcat:lyxvww5tw5fh7mzz543faem6ne
Unsupervised Deep Learning: Taxonomy and algorithms
2022
Informatica (Ljubljana, Tiskana izd.)
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We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. ...
Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. ...
subspace clustering by partitioning data drawn from a union of multiple subspaces. ...
doi:10.31449/inf.v46i2.3820
fatcat:35wje347s5ar3ixzkeqgxc3cbu
SUPERVISED MANIFOLD-LEARNING ALGORITHM FOR POLSAR FEATURE EXTRACTION AND LULC CLASSIFICATION
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, a supervised manifold-learning method is proposed for PolSAR feature extraction and classification. ...
In addition, the spatial details can be well preserved, demonstrating the superior performance of the proposed method. ...
To make the best of the information of training samples while extracting the intrinsic features, this paper proposes a supervised manifold-learning algorithm for PolSAR feature extraction and classification ...
doi:10.5194/isprs-archives-xliii-b3-2020-345-2020
fatcat:efhlv25t4nhdxc4g7ahd26aupa
A Multi-view Dimensionality Reduction Algorithm Based on Smooth Representation Model
[article]
2020
arXiv
pre-print
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2019 pre-print arXiv:1910.04439v2 fatcat:af3ptpqqkjhtbbshu45wps6wtiOther Versions
The proposed method aims to find a subspace for the high-dimensional data, in which the smooth reconstructive weights are preserved as much as possible. ...
Then, we extend it to a multi-view version in which we exploits Hilbert-Schmidt Independence Criterion to jointly learn one common subspace for all views. ...
Linear unsupervised dimensionality reduction Neighborhood preserving embedding (NPE) is a popular linear DR method, which aims to preserving the local neighborhood structure of the data. ...
arXiv:1910.04439v3
fatcat:jjckrbttknb2rn274fwtidjube
High Dimensional Correspondences from Low Dimensional Manifolds – An Empirical Comparison of Graph-Based Dimensionality Reduction Algorithms
[chapter]
2011
Lecture Notes in Computer Science
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We discuss the utility of dimensionality reduction algorithms to put data points in high dimensional spaces into correspondence by learning a transformation between assigned data points on a lower dimensional ...
We assume that similar high dimensional feature spaces are characterized by a similar underlying low dimensional structure. ...
A suitable transformation is a hyper-plane-preserving mapping between the subspaces, e. g. an affine mapping. ...
doi:10.1007/978-3-642-22819-3_34
fatcat:c52wn6vr65hbdlds7s2bl7nb5u
Locality preserving embedding for face and handwriting digital recognition
2011
Neural computing & applications (Print)
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In this paper, a novel supervised method, called locality preserving embedding (LPE), is proposed to feature extraction and dimensionality reduction. ...
Most supervised manifold learning-based methods preserve the original neighbor relationships to pursue the discriminating power. ...
Recently, many supervised learning algorithms were developed to learn the embedding subspace in the problem of classification-oriented multi-manifolds learning, including local discriminant embedding ( ...
doi:10.1007/s00521-011-0577-7
fatcat:yv6r5uepgnh3zenjwiiirzanra
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