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Unsupervised 2D Dimensionality Reduction with Adaptive Structure Learning

Xiaowei Zhao, Feiping Nie, Sen Wang, Jun Guo, Pengfei Xu, Xiaojiang Chen
2017 Neural Computation  
In this letter, we propose a new dimensionality reduction model for 2D image matrices: unsupervised 2D dimensionality reduction with adaptive structure learning (DRASL).  ...  Unsupervised 2D Dimensionality Reduction 1353 procedure of dimensionality reduction.  ...  Instead of using the matrix-to-vector transformation, in this letter, we propose a straightforward 2D unsupervised dimensionality reduction model with adaptive structure learning.  ... 
doi:10.1162/neco_a_00950 pmid:28333584 fatcat:mkj7pbuseza77b2qxdd7te7qqq

Comparison of Dimension Reduction Methods for Database-Adaptive 3D Model Retrieval [chapter]

Ryutarou Ohbuchi, Jun Kobayashi, Akihiro Yamamoto, Toshiya Shimizu
2008 Lecture Notes in Computer Science  
This paper focuses on a method to adapt distance measure to the database to be queried by using learning-based dimension reduction algorithms.  ...  Among the dimension reduction methods we tested, non-linear manifold learning algorithms performed better than the other, e.g. linear algorithms such as principal component analysis.  ...  Acknowledgements The authors would like to thank those who created benchmark databases, those who made available codes for their shape features, and those who made available codes for various learning  ... 
doi:10.1007/978-3-540-79860-6_16 fatcat:lo5bcpw25nc77gegimjwdy7eja

Breast image feature learning with adaptive deconvolutional networks

Andrew R. Jamieson, Karen Drukker, Maryellen L. Giger, Bram van Ginneken, Carol L. Novak
2012 Medical Imaging 2012: Computer-Aided Diagnosis  
Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships  ...  In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided  ...  Unsupervised Dimension Reduction & Visualization Elastic Embedding Dimension Reduction To inspect the structure of the image similarity space as learned by the SPM kernel, we employed unsupervised, non-linear  ... 
doi:10.1117/12.910710 dblp:conf/micad/JamiesonDG12 fatcat:25h5a4zfe5dizlnrdse4vfgyua

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
These two-number sets start with 00, 01, 02, 03, 04,  ...  SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  AND DEEP LEARNING 0J Estimation of four-dimensional CT-based imaging biomarker of liver fibrosis using finite element method 11313 0K Multilevel survival analysis with structured penalties for imaging  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

Learning Low-Dimensional Representation of Bivariate Histogram Data

Evaldas Vaiciukynas, Matej Ulicny, Sepideh Pashami, Slawomir Nowaczyk
2018 IEEE transactions on intelligent transportation systems (Print)  
Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings.  ...  Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data  ...  EXPERIMENTS The first set of experiments compares 2D, 3D, 6D and 10D representations obtained using nine different unsupervised dimensionality reduction techniques.  ... 
doi:10.1109/tits.2018.2865103 fatcat:edxs5srebbdrpgllsncl6kdkwm

A modular network scheme for unsupervised 3D object recognition

Satoshi Suzuki, Hiroshi Ando
2000 Neurocomputing  
This paper presents an unsupervised learning scheme for recognizing 3D objects from their 2D projected images.  ...  We can formally derive an unsupervised learning algorithm based on the adaptive mixture model [9}11] .  ...  ective in clustering 2D object views compared with the traditional K-means algorithm.  ... 
doi:10.1016/s0925-2312(99)00148-4 fatcat:nrmzhsdmdng45m7rllnobnvzqi

Locality Sensitive Discriminative Unsupervised Dimensionality Reduction

Yun-Long Gao, Si-Zhe Luo, Zhi-Hao Wang, Chih-Cheng Chen, Jin-Yan Pan
2019 Symmetry  
corresponding Laplacian matrix to build a novel adaptive graph learning method, namely locality sensitive discriminative unsupervised dimensionality reduction (LSDUDR).  ...  As a result, the learned graph shows a clear block diagonal structure so that the clustering structure of data can be preserved.  ...  In this paper, we propose a novel adaptive graph learning method, namely locality sensitive discriminative unsupervised dimensionality reduction (LSDUDR), which aims to uncover the intrinsic topology structures  ... 
doi:10.3390/sym11081036 fatcat:itrnii7lc5d7nkwuea3uy5tsoe

An effective framework for supervised dimension reduction

Khoat Than, Tu Bao Ho, Duy Khuong Nguyen
2014 Neurocomputing  
We consider supervised dimension reduction (SDR) for problems with discrete inputs.  ...  In this paper, we propose a novel framework for SDR with the aims that it can inherit scalability of existing unsupervised methods, and that it can exploit well label information and local structure of  ...  Figure 2 : 2 Laplacian embedding in 2D space.  ... 
doi:10.1016/j.neucom.2014.02.017 fatcat:pw7wiom53ben7ljmof5r2mxt44

A hybrid supervised ANN for classification and data visualization

Chee Siong Teh, Zahan Tapan Md. Sarwar
2008 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)  
with supervised ANNs such as LVQ.  ...  Beside LVQwithAC was able to provide data topology, data structure, and inter-neuron distance preserve visualization.  ...  Section 2 of this paper describes LVQ and Adaptive Coordinates (AC) with proposed modification in adaptation criteria. In section 3, integration between LVQ and AC (LVQwithAC) is presented.  ... 
doi:10.1109/ijcnn.2008.4633848 dblp:conf/ijcnn/TehS08 fatcat:db4qypvftnajblx3v2nv4ypaxu

Adversarial Learning based Discriminative Domain Adaptation for Geospatial Image Analysis

Nikhil Makkar, Hsiuhan Lexie Yang, Saurabh Prasad
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
First, we approached the problem of unavailable target domain labels with unsupervised domain adaptation and then extended our method for semi-supervised domain adaptation to use a few available labels  ...  We are using adversarial learning to extract discriminative target domain features that are aligned with source domain.  ...  Both spatial features and sequential spectral information together cannot be used by one-dimensional (1D) or two-dimensional (2D) CNNs.  ... 
doi:10.1109/jstars.2021.3132259 fatcat:5ppi25cwirc2bmnlgolauiwga4

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, Youdong Mao, Zhao Zhang
2017 PLoS ONE  
Results: Here we introduce a statistical manifold learning algorithm for unsupervised single-particle deep clustering.  ...  Unsupervised classification may serve as the first step in the assessment of structural heterogeneity.  ...  When linear approximation in dimensionality reduction fails in finding a good low-dimensional representation of high-dimensional data, nonlinear dimensionality reduction can be exploited as an alternative  ... 
doi:10.1371/journal.pone.0182130 pmid:28786986 pmcid:PMC5546606 fatcat:bah4df23fzexfcdxfhaidshusy

Machine learning in chemoinformatics and drug discovery

Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman
2018 Drug Discovery Today  
With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound  ...  databases to design drugs with important biological properties.  ...  However, tight coupling of dimensionality reduction and model fitting can limit utility in unsupervised or semi-supervised problems where knowledge of the outcome variable is missing or incomplete.  ... 
doi:10.1016/j.drudis.2018.05.010 pmid:29750902 pmcid:PMC6078794 fatcat:ckxznjxuujajle6iqycgi74d7i

Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems [article]

W. W. Ahmed, M. Farhat, K. Staliunas, X. Zhang, Y. Wu
2022 arXiv   pre-print
Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process.  ...  The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response  ...  The learned sub-manifolds in 2D latent space are useful to determine the feasibility of the response with a given class of structures and find the best initial seed for inverse-design with neural adaptive  ... 
arXiv:2204.13376v1 fatcat:c2mxduss7zhrleqwdaonl6af3u

Artificial Neural Networks [chapter]

Antonis C. Kakas, David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, Marco Dorigo, Mauro Birattari, Hannu Toivonen, Jon Timmis, Jürgen Branke (+9 others)
2011 Encyclopedia of Machine Learning  
It is now known that some multi-dimensional inputs such as sensory experience have associated three-dimensional locations in the brain.  ...  As in nature, neurons which are weighted and synapses fire when stimulated as the network gradually learns over time.  ...  Learning Types Neural networks typically fall into two categories: supervised and unsupervised.  ... 
doi:10.1007/978-0-387-30164-8_35 fatcat:wf6uosq6djdj3ocmwvj45oscha

Descriptor-free unsupervised learning method for local structure identification in particle packings

Yutao Wang, Wei Deng, Zhaohui Huang, Shuixiang Li
2022 Journal of Chemical Physics  
In this work, we propose an improved unsupervised learning method, which is descriptor-free, for local structure identification in particle packing.  ...  The improved method constructs an autoencoder based on the point cloud network combined with Gaussian mixture models for dimension reduction and clustering.  ...  Thus, the properties of local structures can be learned by the unsupervised learning method.  ... 
doi:10.1063/5.0088056 pmid:35459292 fatcat:netwya2g6zdunl3fmvphtiqnaq
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