A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
Quality-Based Multimodal Classification Using Tree-Structured Sparsity
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
Other Versions
This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. ...
In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity ...
This formulation provides a framework for robust fusion of available sources based on their respective reliability. ...
doi:10.1109/cvpr.2014.524
dblp:conf/cvpr/BahrampourRNJ14
fatcat:akkqnusi2bb4jggr7em3fadp7e
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
[article]
2012
arXiv
pre-print
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
Other Versions
Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. ...
Pixels are assumed to be mixtures of a few materials, called endmembers. ...
Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures ...
arXiv:1202.6294v2
fatcat:4vxq62jxvzfynpb75wvvhw4phq
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
2012
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. ...
Pixels are assumed to be mixtures of a few materials, called endmembers. ...
Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures ...
doi:10.1109/jstars.2012.2194696
fatcat:s66a35xjd5dqzkw5wwihq6ux64
2020 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 17
2021
IEEE/ACM Transactions on Computational Biology & Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
., +, TCBB March-April 2020 704-711 Pancreas Data-Driven Robust Control for a Closed-Loop Artificial Pancreas. 1981 -1993 ...
-Feb. 2020 91-101 Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences. Ranjan, A., +, TCBB Sept. ...
-Dec. 2020 2017-2028 Low-Rank Joint Subspace Construction for Cancer Subtype Discovery. Khan, A., +, TCBB July-Aug. 2020 1290-1302 LSTM-Based End-to-End Framework for Biomedical Event Extraction. ...
doi:10.1109/tcbb.2020.3047571
fatcat:x3kmrpexsve6bnjtd3dh6ntkyy
A review of novelty detection
2014
Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Popular fuzzy-clustering algorithms are the fuzzy versions of the k-means algorithm with probabilistic and possibilistic descriptions of memberships: fuzzy c-means [203] and possibilistic c-means [204 ...
The authors propose a framework to overcome this problem, which involves exploring subspaces of the data, training a separate model for each subspace, and then fusing the decision variables produced by ...
doi:10.1016/j.sigpro.2013.12.026
fatcat:ha6kc4bzhbajxbo2mdyh5cw5hu
Biclustering on expression data: A review
2015
Journal of Biomedical Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. ...
Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. ...
The clustering algorithm used in PSB is a variation of the Improved Possibilistic Clustering (IPC) by Zhang and Leung [48] , which mixes possibilistic and probabilistic approaches. ...
doi:10.1016/j.jbi.2015.06.028
pmid:26160444
fatcat:3w5p45w4zzebxmnphdue67ueny
Editorial: Advanced Deep-Transfer-Leveraged Studies on Brain-Computer Interfacing
2021
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is text/html.
In particular, they developed a possibilistic clustering in Bayesian framework with interclass competitive learning to determine antecedent parameters of fuzzy rules. ...
For example, Huang et al. proposed a classification method using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs). ...
doi:10.3389/fnins.2021.733732
pmid:34489637
pmcid:PMC8417065
fatcat:roe6jkueefa5pbtv3ycaishwgu
Cross-domain, soft-partition clustering with diversity measure and knowledge reference
2016
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
effectiveness as well as strong parameter robustness in the target domain, (3) TI-KT-CM refers merely to the historical cluster centroids, whereas TII-KT-CM simultaneously uses the historical cluster ...
In order to address this challenge, the quadratic weights and Gini-Simpson diversity based fuzzy clustering model (QWGSD-FC), is first proposed as a basis of our work. ...
He is now working at Case Western Reserve University, Cleveland, Ohio, USA as a research scholar and doing research in medical image processing. ...
doi:10.1016/j.patcog.2015.08.009
pmid:27275022
pmcid:PMC4892128
fatcat:gow2zhhgxbeqlkhmpck6pwkvgu
Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
2021
Frontiers in Energy Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is text/html.
This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. ...
In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. ...
A Possibilistic Fuzzy C-Means Clustering Algorithm. IEEE Trans. ...
doi:10.3389/fenrg.2021.751066
fatcat:bzniyxsgofh3pltcnrxd6avhbq
A review of clustering techniques and developments
2017
Neurocomputing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. ...
There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. ...
Acknowledgement The authors would like to thank the anonymous reviewers for their valuable suggestions and comments to improve the quality of the paper. ...
doi:10.1016/j.neucom.2017.06.053
fatcat:z2yzjsdwgnbzbam5bg3s4lu6ny
Representations for Cognitive Vision:A Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches
2008
ELCVIA Electronic Letters on Computer Vision and Image Analysis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
While global PCA and related subspace approaches (e.g. ...
Some success was reported for very narrowly limited cases, for instance, by Zerroug and Nevatia [169] for a few special types of generalized cones under orthographic projection. ...
[37] presented their k-fan model, where k denotes the number of parts that are fully connected to all other parts in the model. ...
doi:10.5565/rev/elcvia.240
fatcat:rtw6iuc5cvfjxkrsdxvocm5vau
NN-EVCLUS: Neural Network-based Evidential Clustering
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 accepted arXiv:2009.12795v2 fatcat:ovf33cysbff75ofapk6swtu2jy
The neural network can be paired with a one-class support vector machine to make it robust to outliers and allow for novelty detection. ...
Comparative experiments show the superiority of N-EVCLUS over state-of-the-art evidential clustering algorithms for a range of unsupervised and constrained clustering tasks involving both attribute and ...
∂βk ∂γi ∂βk ∂γj ∂βk
for k ∈ {0, 1}. ...
arXiv:2009.12795v1
fatcat:7sjgq642ijgzbimulidv53oavu
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms
2021
Journal of Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. ...
The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic. ...
A hyperplane is an (N − 1)-dimensional subspace for an N-dimensional space. ...
doi:10.3390/jimaging7090190
pmid:34564116
pmcid:PMC8466003
fatcat:2r2va44qe5hzhmc6pfysuzphlu
Survey of Clustering Algorithms
2005
IEEE Transactions on Neural Networks
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
, and bioinformatics, a new field attracting intensive efforts. ...
Data analysis plays an indispensable role for understanding various phenomena. ...
ACKNOWLEDGMENT The authors would like to thank the Eisen Laboratory in Stanford University for use of their CLUSTER and TreeView software and Whitehead Institute/MIT Center for Genome Research for use ...
doi:10.1109/tnn.2005.845141
pmid:15940994
fatcat:v5xnatqzlrhbhkm47w75jnepde
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data
2019
Machine Learning
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
relevant features. e fuzzy clustering algorithm can be used to clustered data using different fuzzy clustering algorithms, such as possibilistic c-means, fuzzy possibilistic c-means, or possibilistic ...
subset (for example, 65% samples). 2.1c: Create a dissimilarity matrix and follow the hierarchical clustering procedure. 2.1d: Get k clusters. 2.1e: Determine whether Ai ⊂ Aj, then randomly select a subspace ...
distance between the ith sample and all of the samples included in X j , and b(i) is the minimum average distance between the ith sample and all of the samples clustered in = ≠ X k c k j s i k ( 1,.. ...
doi:10.1007/s10994-019-05810-5
fatcat:nulmjvxvwjgojfoe2ywv3pjrpu
« Previous
Showing results 1 — 15 out of 33 results