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Self-Organizing Map Learning with Momentum
2016
Computer and Information Science
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<p class="zhengwen"><span lang="EN-GB">Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. ...
Since self-organizing map may be trapped in a local optimum, so we introduce momentum into the learning process thus the movement of a neuron may jump over local optimum. ...
Self-organizing map (Kohonen, 1982) is a competitive learning based clustering neural networks. Neurons on the map compete with each other for each input data. ...
doi:10.5539/cis.v9n1p136
fatcat:4ary5rl74railm3bljv4jccab4
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data
[article]
2015
arXiv
pre-print
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In this paper, we propose an innovative and effective clustering framework based on self-adaptive labeling (CSAL) which integrates clustering and classification on unlabeled data. ...
Experiments are conducted on publicly data sets to test different combinations of clustering algorithms and classification models as well as various training data labeling methods. ...
[17] also introduced an algorithm for learning from labeled and unlabeled documents based on the combination of EM and a Naive Bayes classifier. ...
arXiv:1502.05111v1
fatcat:fgrd2zmivbbtlknngdiviuln5m
Self-supervised Deep Subspace Clustering for Hyperspectral Images with Adaptive Self-expressive Coefficient Matrix Initialization
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Index Terms-Deep subspace clustering (DSC), hyperspectral image (HSI), self-expressive, self-supervised, subspace clustering (SC). ...
Then, an adaptive self-expressive coefficient matrix initialization strategy is designed to reduce the dictionary redundancy, where the spectral similarity between each target sample and its neighbors ...
initialization module is used to provide a good initialized self-expressive coefficient matrix for the self-expressiveness module; d) self-supervised learning-based classification module classifies the ...
doi:10.1109/jstars.2021.3063335
fatcat:llkh4sqw7nblrpsbdk7ooe7d3i
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.15037v1 fatcat:jdzgbsc4lrfnbmxa23d22h56f4
In contrast to previous approaches taking the weights of a fully connected layer as the self-expressive coefficients, we propose to learn an energy-based network to obtain the self-expressive coefficients ...
In this paper, we propose a new deep subspace clustering framework, motivated by the energy-based models. ...
Finally, we approximate the representation z A i using the dictionary Z B and the learned self-expressive coefficients c i where is inner-product operation coefficient learning module based on energy based ...
arXiv:2110.15037v2
fatcat:fri55joh7fdmpjxjtjkv32ogh4
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.02134v1 fatcat:qlidx5gyhzel7ooq4hycrbufau
Our approach learns local dictionaries that represent a data point with reconstruction coefficients supported on the probability simplex. ...
The dictionaries are learned using algorithm unrolling, an increasingly popular technique for structured deep learning. ...
Our Contributions The main contribution of our work is a novel manifold learning and clustering algorithm based on sparse self-representation that overcomes both of the obstacles described in the previous ...
arXiv:2012.02134v2
fatcat:jw7nfmtztra7hcg2nhj7457ucu
Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients
2005
Proceedings of the third ACM international workshop on Video surveillance & sensor networks - VSSN '05
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The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. ...
This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. ...
The system architecture and trajectory learning algorithm is presented in section 3 within the framework of a self-organising map. ...
doi:10.1145/1099396.1099404
fatcat:eshvy3oxgvhh5cpawf6rfhyqu4
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. ...
Conclusion In this paper, we presented a comprehensive overview of nonlinear subspace clustering (nonlinear SC) approaches, our main focus being on algorithms based on self-expressiveness. ...
arXiv:2103.10656v1
fatcat:vtlb3d337fgixkumu5faidisrm
A Self-Learning Diagnosis Algorithm Based on Data Clustering
2016
Intelligent Control and Automation
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How to cite this paper: Tretyakov, D. (2016) A Self-Learning Diagnosis Algorithm Based on Data Clustering. Intelligent Control and Automation, 7, 84-92. http://dx. ...
Abstract The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. ...
The diagnostic algorithm has to use the signals coming from sensors for self-learning. In [10] the learning methods based on association are used for generating new rules from incoming data. ...
doi:10.4236/ica.2016.73009
fatcat:cwnzxkeskvb7nck4obr7elrp3e
DSCD:A Novel Deep Subspace Clustering Denoise Network For Single-cell Clustering
2020
IEEE Access
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SPECTRAL CLUSTERING The spectral clustering algorithm is based on the spectral graph theory. ...
NMF [30] is an algorithm for non-negative matrix factorization, which learns holistic, not parts-based, representations. ...
doi:10.1109/access.2020.3001986
fatcat:wexzgp6u4zek7n4uw57aavk6qe
Centroid neural network for unsupervised competitive learning
2000
IEEE Transactions on Neural Networks
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An unsupervised competitive learning algorithm based on the classical -means clustering algorithm is proposed. ...
The CNN algorithm requires neither a predetermined schedule for learning coefficient nor a total number of iterations for clustering. ...
CONCLUSION The CNN algorithm based on the -means clustering algorithm is proposed. The proposed CNN algorithm has a strong connection with some of the conventional unsupervised learning algorithms. ...
doi:10.1109/72.839021
pmid:18249781
fatcat:pyzhrl723zdahfxwtd3g57qj4u
Motion Trajectory Learning in the DFT-Coefficient Feature Space
2006
Fourth IEEE International Conference on Computer Vision Systems (ICVS'06)
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The DFT coefficients are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. ...
In this paper we propose a novel vision system for clustering and classification of object-based video motion clips using spatiotemporal models. ...
The system architecture and trajectory learning algorithm is presented in section 3 within the framework of a self-organising map. ...
doi:10.1109/icvs.2006.41
dblp:conf/icvs/NaftelK06
fatcat:5o6rywvkebcx3cwlx7bnffisry
Limitations of Using Constraint Set Utility in Semi-Supervised Clustering
2015
European Conference on Principles of Data Mining and Knowledge Discovery
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We compare some of these algorithms experimentally, and observe that their performances vary significantly, depending on the data set and constraints. ...
Semi-supervised clustering algorithms allow the user to incorporate background knowledge into the clustering process. ...
For example, one can learn a separate metric for each cluster, or a global one. ...
dblp:conf/pkdd/CraenendonckB15
fatcat:5wfbst6x3rdpdgy7yhbjctasou
Towards Self-adaptive Defect Classification in Industrial Monitoring
2020
Proceedings of the 9th International Conference on Data Science, Technology and Applications
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We compare well-known heuristics, based on k-means, hierarchical-and density based clustering and configure them to work best under the given circumstances. ...
We then cluster the incoming defect data with stream clustering algorithms in order to identify structures, tendencies and anomalies. ...
In order to answer this question we propose a clustering based approach for unsupervised learning on a representative industrial image dataset. ...
doi:10.5220/0009893003180327
dblp:conf/data/MargrafHBG20
fatcat:c4nhum2okbed7nyjw6xm3u3jjy
Method of Image De-Noising Based on Non-Noisy Atoms Self Adaptive and Sparse Representation
2016
International Journal of Signal Processing, Image Processing and Pattern Recognition
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In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper ...
Firstly, this algorithm is adopted to obtain selfadaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistical features ...
In recent years, the de-noising method based on dictionary learning [1] [2] [3] [4] and the de-noising method based on nonlocal self-similarity [5] [6] [7] [8] in the image processing field are widely ...
doi:10.14257/ijsip.2016.9.8.35
fatcat:rzslafhndrhdhiwge7hxgy27re
Dynamic CR-based Classifiers Ensemble based on Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Moreover, a new method of constructing the Laplacian matrix using kernel CR coefficients is proposed for clustering based on subspace clustering and CR theory. ...
This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. ...
Subspace Clustering Based on Spectral Clustering The subspace clustering algorithm based on spectral clustering is one of the subspace clustering algorithms. ...
doi:10.1109/jstars.2022.3158761
fatcat:nopnupirovd4ji6canfoaaro2m
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