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Density peaks clustering algorithm based on weighted k-nearest neighbors and geodesic distance
2020
IEEE Access
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Therefore, a major motivation of this paper is to extend the DPC to manifold datasets to expand the scope of application, and improve the clustering performance on manifold and non-manifold datasets. ...
In 2014, density peaks clustering (DPC) was proposed by Rodriguez and Laio [8] , resulting in density-based clustering algorithms attracting wider attention and application. ...
The experimental results on artificial and real-world datasets, including image datasets, show that the DPC-WKNN-GD improves the clustering performance and significantly outperforms comparison algorithms ...
doi:10.1109/access.2020.3021903
fatcat:drlskzss5jf23noasb6bkbzsgi
High-veracity functional imaging in scanning probe microscopy via Graph-Bootstrapping
2018
Nature Communications
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To meet this challenge, we present a data-driven approach, Graph-Bootstrapping, based on low-dimensional manifold learning of the full SPM spectra and demonstrate its successes for high-veracity mechanical ...
mapping on a mixed polymer thin film and resolving irregular hydration structure of calcite at atomic resolution. ...
The authors gratefully acknowledge Hagen Söngen and Angelika Kühnle for help and discussion on 3D-AFM datasets. ...
doi:10.1038/s41467-018-04887-1
pmid:29930246
pmcid:PMC6013493
fatcat:irlqbqp53zbgjfn5h6xhoierna
Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding
2021
IEEE Access
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The most remarkable applications are based on the fault diagnosis classification model. ...
Then, based on the representation learned from the autoencoders, the local manifold learning method UMAP is used to search for more clustering manifold structures. ...
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. ...
doi:10.1109/access.2021.3059459
fatcat:hirjpl7c2bbz3a5nhut6rxiuae
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
[article]
2022
arXiv
pre-print
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The competition asked participants to provide implementations of machine learning algorithms on manifolds that would respect the API of the open-source software Geomstats (manifold part) and Scikit-Learn ...
This paper describes the design of the challenge and summarizes its main findings. ...
In tandem with a sampling module, we could consider extending Geomstats' probability module that provides probability densities on manifolds. ...
arXiv:2206.09048v1
fatcat:ghgwtpjqyzhrxmco7bk24satuy
Fundamentals
[chapter]
2018
Projection-Based Clustering through Self-Organization and Swarm Intelligence
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., 1990] can be used to define neighborhoods based on density. ...
., 2000] or local linear embedding (LLE) [Roweis/Saul, 2000] that are designed to find a manifold 5 that represents a given set of high-dimensional data 6 are called manifold learning methods. ...
doi:10.1007/978-3-658-20540-9_2
fatcat:fgrf47vxvfa6bir43hbxjvmlce
Distilling nanoscale heterogeneity of amorphous silicon using tip-enhanced Raman spectroscopy (TERS) via multiresolution manifold learning
2021
Nature Communications
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To project the high dimensional TERS imaging to a two-dimensional manifold space and categorize amorphous silicon structure, we developed a multiresolution manifold learning algorithm. ...
However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. ...
The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). ...
doi:10.1038/s41467-020-20691-2
pmid:33495465
fatcat:amvuy7dy5vhahd4pxpxwfjc7xe
Beyond Sentiment: The Manifold of Human Emotions
[article]
2013
arXiv
pre-print
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Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains. ...
We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. ...
Our fitted model bears close similarities to models developed within the psychological literature, based on human survey data. ...
arXiv:1202.1568v2
fatcat:z5mifnewzzhp5ps24dyhkk6you
Interactive Exploration of Large-Scale UI Datasets with Design Maps
2021
Interacting with computers
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Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? ...
Overall, designers find Design Maps supporting their creativity (avg. ...
They pointed out that they would like to interact with a design space visualization based on some UI similarity. ...
doi:10.1093/iwcomp/iwab006
fatcat:msdbbhy52bfwpo4ry3onxhuqka
Information Geometry-Based Fuzzy-C Means Algorithm for Cooperative Spectrum Sensing
2020
IEEE Access
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Moreover, a novel clustering algorithm, namely Riemannian distance based Fuzzyc means clustering (RDFCM) algorithm, is developed to cluster the samples on manifold for obtaining a classifier, which is ...
Based on the information geometry (IG) theory, the covariance matrix of the signal matrix is mapped into a point on a manifold. Then, the sample points on the manifold are collected as a data set. ...
Subsequently, the RDFCM algorithm is designed, which clustering directly on the manifold space. ...
doi:10.1109/access.2020.3019422
fatcat:cjvm67nbjng3de4i2qyv4zs3zy
Methods of Projection
[chapter]
2018
Projection-Based Clustering through Self-Organization and Swarm Intelligence
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Thrun, Projection-Based Clustering through Self-Organization and Swarm Intelligence, https://doi. ...
Venna et al. argued that "manifold learning methods are not necessarily good for [...] visualization [...] since they have been designed to find a manifold, not compress it into a lower dimensionality" ...
Using transitive closure for these weighted distances allows classical clustering algorithms (AU*clustering) to actually perform distance-and density-based clustering, taking into account the complex structure ...
doi:10.1007/978-3-658-20540-9_4
fatcat:kmxwfd7tmrc3doylh6sij2zlfe
Machine Learning in Chemical Engineering : A Perspective
2021
Chemie - Ingenieur - Technik (2021). doi:10.1002/cite.202100083
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The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio-)catalysts, and functional materials. ...
, and (6) creativity. ...
density estimation for applications with unknown distributions, e.g., for data smoothing [23] . ...
doi:10.18154/rwth-2021-09826
fatcat:7tlvcx22urd27fpjfbw4iqhr7a
Cluster Density Properties Define a Graph for Effective Pattern Feature Selection
2020
IEEE Access
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The graph design in this method promotes feature relevance, downgrades redundancy, and is robust to outliers and cluster imbalances. ...
Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. ...
The grid-based algorithm CLIQUE [44] , which developed subspace clustering, recursively investigates the set of possible subspaces based on an a priori-like method and then retains grid cells with a density ...
doi:10.1109/access.2020.2981265
fatcat:vmgfcfexxbavjny3ocsrbpzvrm
Advancing Performability in Playable Media: A Simulation-based Interface as a Dynamic Score
2014
EAI Endorsed Transactions on Creative Technologies
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Problems of designing playable media with non-game orientation are stated as the problems of designing a platform for creative explorations and creative expressions. ...
When designing playable media with non-game orientation, alternative play scenarios to gameplay scenarios must be accompanied by alternative mechanics to game mechanics. ...
We wish to thank Hiroki Sayama for sharing his code base to develop the playable implementation. ...
doi:10.4108/ct.1.1.e5
fatcat:t5fswpdrpbamzdrxfjn2ixqtfq
Foundations of Unsupervised Learning (Dagstuhl Seminar 16382)
2017
Dagstuhl Reports
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The goal of the seminar was to initiate a broader and more systematic research on the foundations of unsupervised learning with the ultimate aim to provide more support to practitioners. ...
However, there is currently little formal guidance as to how, when and to what effect to use which unsupervised learning method. ...
The procedure is then shown to be competitive on clustering applications, and moreover is quite stable to a wide range of settings of its tuning parameter. ...
doi:10.4230/dagrep.6.9.94
dblp:journals/dagstuhl-reports/BalcanBUL16
fatcat:gliqlrxzyrbzffssk5t3udw54q
Electronic structure and photophysics of a supermolecular iron complex having a long MLCT-state lifetime and panchromatic absorption
2020
Proceedings of the National Academy of Sciences of the United States of America
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Density functional and domain-based local pair natural orbital coupled cluster [DLPNO-CCSD(T)] theory reveal triplet-state wavefunction spatial distributions consistent with electronic spectroscopic and ...
Here, we engineer supermolecular Fe(II) chromophores based on the bis(tridentate-ligand)metal(II)-ethyne-(porphinato)zinc(II) conjugated framework, previously shown to give rise to highly delocalized low-lying ...
Density functional and domain-based local pair natural orbital coupled cluster [DLPNO-CCSD(T)] theory reveal tripletstate wavefunction spatial distributions consistent with electronic spectroscopic and ...
doi:10.1073/pnas.2009996117
pmid:32788361
fatcat:3fiqkzn7ebakjewzyrdo4uogrm
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