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An unsupervised data projection that preserves the cluster structure
2012
Pattern Recognition Letters
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In this paper we propose a new unsupervised dimensionality reduction algorithm that looks for a projection that optimally preserves the clustering data structure of the original space. ...
Formally we attempt to find a projection that maximizes the mutual information between data points and clusters in the projected space. ...
Another possible research direction is modifying current manifold learning algorithms such as LLE and Isomap such that in addition to learning the local manifold structure, they will also preserve the ...
doi:10.1016/j.patrec.2011.10.012
fatcat:aiefszh22rgnlnzuu6hks5wf5q
Unsupervised Feature Extraction for Reliable Hyperspectral Imagery Clustering via Dual Adaptive Graphs
2021
IEEE Access
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Firstly, low-rank reconstruction and projected learning are incorporated into the proposed framework to improve the data quality and obtain their robust structures. ...
After that, the normalized cut technique is applied to the learned consistent graph to obtain the final unsupervised feature. ...
Locality preserving projection(LPP) in [20] aims to optimally preserve the neighborhood structure of data. ...
doi:10.1109/access.2021.3071425
fatcat:hvxtbpth3bgpdb2y444edy77ea
An Artificial Life Approach for Semi-supervised Learning
[chapter]
2008
Studies in Classification, Data Analysis, and Knowledge Organization
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Their unsupervised movement patterns correspond to structural features of a high dimensional data set. ...
An approach for the integration of supervising information into unsupervised clustering is presented (semi supervised learning). ...
A set of simulated ants moves on the grid's nodes. The ants are used to cluster data objects that are located on the grid. An ant might pick up a data object and drop it later on. ...
doi:10.1007/978-3-540-78246-9_17
fatcat:ahjtfx6edvffbicawjhhu73l44
Globally and Locally Consistent Unsupervised Projection
2014
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we propose an unsupervised projection method for feature extraction to preserve both global and local consistencies of the input data in the projected space. ...
Traditional unsupervised feature extraction methods, such as principal component analysis (PCA) and locality preserving projections (LPP), can only explore either the global or local geometric structures ...
geometric structure in the data, our new method learns an optimal projection to maximize the global covariance Figure 1 : 1 Figure1: Clustering performance on the Dermatology data set in the projected ...
doi:10.1609/aaai.v28i1.8915
fatcat:amvjbxpcpfalrb6khvjfso43xy
Clustering-Based Discriminative Sparsity Preserving Projections for Unsupervised Face Recognition
2017
Innovative Computing Information and Control Express Letters, Part B: Applications
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In this paper, a novel unsupervised dimension reduction algorithm, named clustering-based discriminative sparsity preserving projections (CDSPP), is proposed by integrating cluster analysis and sparse ...
Moreover, CDSPP is an unsupervised dimensionality reduction method, which improves the simplicity of model training. Experiments on AR and Yale-B image datasets demonstrate its effectiveness. ...
The work is partially supported by the National Natural Science Foundation of China (Nos. 61572298, 61373081, 61402268, 61401260) and the Taishan Scholar Project of Shandong, China. ...
doi:10.24507/icicelb.08.01.11
fatcat:ivoujjz2avgbdkmmm5unfpm7zq
Spare Projections with Pairwise Constraints
2012
Procedia Engineering
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embedding model, which encodes the global and local geometrical structures in the data as well as the pairwise constraints. ...
Unlike many existing techniques such as locality preserving projection (LPP) and semi-supervised DR (SSDR), where local or global information is preserved during the DR procedure, SPPC constructs a graph ...
Acknowledgment The research reported in this paper has been partially supported by Natural Science Foundation of Zhejiang Province under Grant No. ...
doi:10.1016/j.proeng.2012.01.084
fatcat:n5cvfjntefbqdn2qt6wvrl5k7e
Genetic Algorithms for Exploratory Data Analysis
[chapter]
2002
Lecture Notes in Computer Science
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In the present work, we propose a new data projection method that uses genetic algorithms to find linear projections, providing meaningful representations of the original data. ...
Data projection is a commonly used technique applied to analyse high dimensional data. ...
The goal of using this criterion is to preserve the class structure of the data in the projected space. ...
doi:10.1007/3-540-70659-3_78
fatcat:lbjemegdunbadd6wz3kaunbc6y
TransRHS: A Representation Learning Method for Knowledge Graphs with Relation Hierarchical Structure
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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The experimental results show that our TransRHS model significantly outperforms all baselines on both tasks, which verifies that the RHS information is significant to representation learning of knowledge ...
Relation Hierarchical Structure (RHS), which is constructed by a generalization relationship named subRelationOf between relations, can improve the overall performance of knowledge representation learning ...
This is because the distances in both the non-linear projection in RDP and the linear projection in SRP SS is well preserved, enabling RDP to effectively learn much more faithful class structure than that ...
doi:10.24963/ijcai.2020/408
dblp:conf/ijcai/WangPSM20
fatcat:lmm3upljhrgjbnq4nk4nsjcxc4
A three-step unsupervised neural model for visualizing high complex dimensional spectroscopic data sets
2010
Pattern Analysis and Applications
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The interdisciplinary research presented in this study is based on a novel approach to clustering tasks and the visualization of the internal structure of high-dimensional data sets. ...
Subsequently, the self organising-map (SOM) is applied, as a topology-preserving architecture used for two-dimensional visualization of the internal structure of such data sets. ...
It shows that both methods are able to identify some degree of internal structure based on the existence of several groups or clusters (two or three depending on the projections) and several sub-clusters ...
doi:10.1007/s10044-010-0187-5
fatcat:hffmlytac5aexfql7scrfflrfy
Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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This paper proposes an unsupervised triplet-induced graph to explore a new type of potential structure at feature level, and incorporates it into simultaneous feature selection and clustering. ...
This term enforces the projection vectors to preserve the relative proximity of original features, which contributes to selecting more relevant features. ...
Xiangwei Kong from Zhejiang University for reviewing an earlier version of this paper. ...
doi:10.1609/aaai.v34i01.5336
fatcat:3woh4jkl3fev3eunrivrgau3oq
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
[chapter]
2012
Lecture Notes in Computer Science
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for an unsupervised learning task. ...
Essentially, the measure evaluates in how far neighborhood relations are preserved if evaluated based on rankings, this way achieving a robustness of the measure against scaling of data. ...
Acknowledgment This work has been supported by the German Science Foundation (DFG) under grants number PI764/6 and HA2719/6-1, and by the center of excellence for cognitive interaction technology (CITEC ...
doi:10.1007/978-3-642-33212-8_1
fatcat:6gvvaq7zgfeahhtzwj5mp7ku3m
Discriminative Partition Sparsity Analysis
2014
2014 22nd International Conference on Pattern Recognition
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data points, meanwhile preserving the natural locality relationship among the data. ...
In each cluster, a number of sparse sub-graphs are computed via the 1-norm constraint to optimally represent the intrinsic data structure. ...
Then an 1 sub-graph is constructed to preserve the data locality structure in each cluster and finally all the subgraphs are merged to compute the projection. ...
doi:10.1109/icpr.2014.283
dblp:conf/icpr/LiuS14
fatcat:zlsrtvw5jbddlnl4q762ny6cby
caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data
2008
BMC Bioinformatics
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Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. ...
Results: In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. ...
This work is supported by the National Institutes of Health under Grants CA109872, NS29525, CA096483, EB000830 and caBIG™. ...
doi:10.1186/1471-2105-9-383
pmid:18801195
pmcid:PMC2566986
fatcat:53naul2uf5dyjktgslc2ahw7o4
Joint Multi-view Unsupervised Feature Selection and Graph Learning
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2204.08247v1 fatcat:uegepff2ebcivnw36esrrz526qOther Versions
The cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. ...
Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency ...
ACKNOWLEDGMENTS This work was supported by the NSFC (61976097, 61876193, & U1811263) and the Natural Science Foundation of Guangdong Province (2021A1515012203). ...
arXiv:2204.08247v3
fatcat:rz46zerp55cldbmuchol5vrc6q
Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information
2014
IEICE transactions on information and systems
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In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. ...
the original data are kept as much as possible. ...
Acknowledgments JS was supported by the Ph.D. scholarship from Prince of Songkla University, and MS is supported by MEXT KAK-ENHI 25700022 and AOARD. ...
doi:10.1587/transinf.2014edl8111
fatcat:7hvgc7q7fbdtflalqpift5fjmy
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