Multi-manifold clustering: A graph-constrained deep nonparametric method

Ye, Xulun; Zhao, Jieyu

doi:10.1016/j.patcog.2019.04.029

Cited by 20 publications

(3 citation statements)

References 18 publications

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“…Autoencoder. Autoencoder is an artificial neural network for unsupervised learning, which consists of three layers: input layer, output layer, and hidden layer [39]. At present, there are two main applications of autoencoders, one is data denoising, and the other is for visual and dimension reduction.…”

Section: Ae-elm Networkmentioning

confidence: 99%

Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network

Sun

Gao

2022

Computational Intelligence and Neuroscience

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Aiming at the influence of different working conditions on recognition accuracy in remote sensing image recognition, this paper adopts hierarchical strategy to construct a network. Firstly, in order to establish the classification relationship between different samples, labeled samples are used for classification. A Logistic-T-distribution-Sparrow Search Algorithm-Least Squares Support Vector Machines (LOG-T-SSA-LSSVM) classification network is proposed. LOG-T-SSA algorithm is used to optimize parameters in LSSVM to establish a better network to achieve accurate classification between sample sets and then identify according to different categories. Through UCI dataset test, the accuracy of LOG-T-SSA-LSSVM network classification is significantly improved compared with that of contrast network. The autoencoder is integrated with Extreme Learning Machine, and the autoencoder is used to realize data compression. The advantages of Extreme Learning Machine (ELM) network, such as less training parameters, fast learning speed, and strong generalization ability, are fully utilized to realize efficient and supervised recognition. Experiments verify that the autoencoder-extreme learning machine (AE-ELM) network has a good recognition effect when the sigmoid activation function is selected and the number of hidden layer neurons are 2000. Finally, after image recognition under different working conditions, it is proved that the recognition accuracy of AE-ELM based on LOG-T-SSA-LSSVM classification is significantly improved compared with traditional ELM network and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) network.

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Section: Ae-elm Networkmentioning

confidence: 99%

Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network

Sun

Gao

2022

Computational Intelligence and Neuroscience

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“…Such frameworks have been recently introduced based on well-known algorithms, such as spectral clustering and local tangent space estimation (Wang et al, 2010(Wang et al, , 2011Gong et al, 2012), LLE (Hettiarachchi and Peters, 2015), ISOMAP (Fan et al, 2012;Yang et al, 2016;Li et al, 2017;Mahapatra and Chandola, 2017) and local PCA (Arias-Castro et al, 2017). Other approaches use less classical techniques such as tensor voting (Mordohai and Medioni, 2010;Medioni, 2015, 2016), variational autoencoders (Ye and Zhao, 2019), or multi-agent flow (Shen and Han, 2016).…”

Section: Multi-manifold and Manifold Alignment Learningmentioning

confidence: 99%

Lizard Brain: Tackling Locally Low-Dimensional Yet Globally Complex Organization of Multi-Dimensional Datasets

Bac

Zinovyev

2020

Front. Neurorobot.

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Machine learning deals with datasets characterized by high dimensionality. However, in many cases, the intrinsic dimensionality of the datasets is surprisingly low. For example, the dimensionality of a robot's perception space can be large and multi-modal but its variables can have more or less complex non-linear interdependencies. Thus multidimensional data point clouds can be effectively located in the vicinity of principal varieties possessing locally small dimensionality, but having a globally complicated organization which is sometimes difficult to represent with regular mathematical objects (such as manifolds). We review modern machine learning approaches for extracting low-dimensional geometries from multi-dimensional data and their applications in various scientific fields.

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“…At present, the above cross-manifold clustering is still a hard topic [23], though there have been two types of manifold clustering methods: spectral-type clustering [24], [25], [26], [27] and flat-type clustering [9], [10]. Spectral-type clustering assigns the samples into clusters by the similarity graph, which is the local neighborhood relationship.…”

Section: Introductionmentioning

confidence: 99%

Multiple Flat Projections for Cross-manifold Clustering

Bai,

Shao,

Chen

et al. 2020

Preprint

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Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods.

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Multi-manifold clustering: A graph-constrained deep nonparametric method

Cited by 20 publications

References 18 publications

Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network

Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network

Lizard Brain: Tackling Locally Low-Dimensional Yet Globally Complex Organization of Multi-Dimensional Datasets

Multiple Flat Projections for Cross-manifold Clustering

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