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SAM 2020 Author Index

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Network for Radar HRRP Noncooperative Target Recognition Wiesel, Ami R04.5 Spectral Algorithm for Shared Low-rank Matrix Regressions Wu, Chen R06.2 Coded Aperture Imaging Based on Selected Reference  ...  Linear Arrays With Mutual Coupling SS13.5 Transmit Beampattern Design for MIMO Radar with One-bit DACs via Block-Sparse SDR Chun-hua, Chu R07.2 MIMO Radar Waveform Joint Optimization Design in  ... 
doi:10.1109/sam48682.2020.9104397 fatcat:cfp5gsikrzabhhcnkalahjkxze

Document clustering via adaptive subspace iteration

Tao Li, Sheng Ma, Mitsunori Ogihara
2004 Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04  
Document clustering has long been an important problem in information retrieval.  ...  Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.  ...  Jieping Ye for providing useful insights on Section 2.3. We are also grateful to the conference reviewers for their helpful comments and suggestions.  ... 
doi:10.1145/1008992.1009031 dblp:conf/sigir/LiMO04 fatcat:5mvq4atlgvh25irqrqwixcrgti

Deep understanding of big multimedia data

Xiaofeng Zhu, Chong-Yaw Wee, Minjeong Kim
2020 Neural computing & applications (Print)  
In [7] , Tan et al. propose a new spectral clustering method based on mutual k-nn.  ...  In [9], Li et al. propose an unsupervised nonlinear feature selection method via kernel function.  ...  In [7] , Tan et al. propose a new spectral clustering method based on mutual k-nn.  ... 
doi:10.1007/s00521-020-04885-9 fatcat:wiqlyp5kebdotkti7ljt3cr66m

Sparse preserving feature weights learning

Guangsheng Xia, Hui Yan, Jian Yang
2016 Neurocomputing  
It adaptively determines the locality based on sparse representation, instead of fixing the k-nearest neighbors in the original feature space. (2) SPFW selects the most discriminative feature subset from  ...  It adaptively determines the locality based on sparse representation, instead of fixing the k-nearest neighbors in the original feature space. (2) SPFW selects the most discriminative feature subset from  ...  selection methods perform much better than All Features, which Number of the Selected Features Normalized Mutual Information (%) Number of the Selected Features Normalized Mutual Information (%) 200  ... 
doi:10.1016/j.neucom.2015.12.020 fatcat:evugk4mxpjhmrjw3q4rxwsyi5u

A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search

Shijin Li, Jianbin Qiu, Xinxin Yang, Huan Liu, Dingsheng Wan, Yuelong Zhu
2014 Engineering applications of artificial intelligence  
Firstly, spectral clustering is utilized to cluster all the training samples, which produces the prototypical spectral curves of each cluster.  ...  Then a set of initial candidate bands are obtained based on the extraction of key points from the processed hyperspectral curves, which preserve discriminative information and narrow down the candidate  ...  This paper has put forward a novel band selection method based on time series key point extraction, with spectral clustering as the preprocessing step, filtering with conditional mutual information and  ... 
doi:10.1016/j.engappai.2013.07.010 fatcat:kxukw7m2gjaj7c2vzabahrozzm

Learning With $\ell ^{1}$-Graph for Image Analysis

Bin Cheng, Jianchao Yang, Shuicheng Yan, Yun Fu, T.S. Huang
2010 IEEE Transactions on Image Processing  
Extensive experiments on three real-world datasets show the consistent superiority of 1 -graph over those classic graphs in data clustering, subspace learning, and semi-supervised learning tasks.  ...  ., data clustering, subspace learning, and semisupervised learning, are derived upon the 1 -graphs.  ...  + K-MEANS ON THE FOREST COVERTYPE DATABASE TABLE III CLUSTERING ACCURACIES (NORMALIZED MUTUAL INFORMATION/NMI AND ACCURACY/AC) FOR SPECTRAL CLUSTERING ALGORITHMS BASED ON`-GRAPH, GAUSSIAN-KERNEL GRAPH  ... 
doi:10.1109/tip.2009.2038764 pmid:20031500 fatcat:lbju2dvonvb2hijji55ueqme6a

A survey of band selection techniques for hyperspectral image classification

Shrutika Sawant, Manoharan Prabukumar
2020 Journal of Spectral Imaging  
Focusing on the classification task, this article provides an extensive and comprehensive survey on band selection techniques describing the categorisation of methods, methodology used, different searching  ...  Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes.  ...  Mutual information The mutual information between two bands, B m and B n , with joint probability distribution p(b m ,b n ) and marginal probability distribution p(b m ) and p(b n ), can be expressed as  ... 
doi:10.1255/jsi.2020.a5 fatcat:cvibjoofbbd6jpu4ij626wigdy

Unsupervised Feature Selection via Multi-step Markov Transition Probability [article]

Yan Min, Mao Ye, Liang Tian, Yulin Jian, Ce Zhu, Shangming Yang
2020 arXiv   pre-print
On the contrary, the features that least maintain the loose data structure are selected. And the two ways can also be combined. Thus three algorithms are proposed.  ...  Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability.  ...  [12] proposed a unified framework for feature selection based on spectral graph theory, which is based on general similarity matrix.  ... 
arXiv:2005.14359v1 fatcat:5rs2v2d2rzhqnjvk4auluzwncu

Regularized Sparse Band Selection via Learned Pairwise Agreement

Zhixi Feng, Shuyuan Yang, Xiaolong Wei, Quanwei Gao, Licheng Jiao
2020 IEEE Access  
Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned  ...  The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement  ...  A few clustering based band selection techniques for hyperspectral images exist in the literature e.g., Ward's linkage strategy using divergence (WaLuDi) [19] , or using mutual information (WaLuMI) [  ... 
doi:10.1109/access.2020.2971556 fatcat:72u7qlvq75cpnatvv773xlpxcq

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction [article]

Andrea Marinoni, Saloua Chlaily, Eduard Khachatrian, Torbjørn Eltoft, Sivasakthy Selvakumaran, Mark Girolami, Christian Jutten
2021 arXiv   pre-print
By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified.  ...  In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue.  ...  Specifically, a joint graph based on connections weighted according to local (Gaussian kernel) and global (mutual information) metrics is employed to adaptively cluster the most significant features for  ... 
arXiv:2105.03682v1 fatcat:jznrxvrir5hs5bq4qxhzvmzpi4

[SAM 2020 Title Page]

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
33 1570620259 Block-Sparse Signal Recovery Based on Adaptive Matching Pursuit via Spike and Slab Prior C 34 1570620530 Chance Constrained Beamforming for Joint Radar-Communication Systems 35 1570618057  ...  Subspace Clustering with Linear Subspace-Neighborhood-Preserving Data Embedding 144 1570620539 Spectral Algorithm for Shared Low-rank Matrix Regressions 145 1570620900 Study on Coding Scheme with EPC-MIMO  ... 
doi:10.1109/sam48682.2020.9104267 fatcat:erntqdmhdrdspcrkvjowtplyyq

Information Theoretic Pairwise Clustering [chapter]

Avishay Friedman, Jacob Goldberger
2013 Lecture Notes in Computer Science  
We utilize this probabilistic model to define a novel clustering cost function that is based on maximizing the mutual information between consecutively visited clusters of states of the Markov chain defined  ...  This view forms a probabilistic interpretation of spectral clustering methods.  ...  The probabilistic interpretation of spectral clustering, based on a Markov random walk, is used to associate a distribution with each data point via the corresponding conditional distribution row in the  ... 
doi:10.1007/978-3-642-39140-8_7 fatcat:btw42jrp4jeqvaibnjw4so474y

Guest Editorial Computational Imaging for Earth Sciences

Shuchin Aeron, Eric L. Miller, Melba Crawford, Alison Malcom, Andreas Reigber, Jocelyn Chanussot
2017 IEEE Transactions on Computational Imaging  
In "Unsupervised data driven feature extraction by means of mutual information maximization," a robust approach for feature selection is presented for remote sensing data that takes into account the complex  ...  The paper "Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images" proposes an efficient implementation for feature selection, which  ... 
doi:10.1109/tci.2017.2694978 fatcat:7jfk5xhrlner3forntsrzixeti

Pairwise clustering based on the mutual-information criterion

Amir Alush, Avishay Friedman, Jacob Goldberger
2016 Neurocomputing  
We utilize this probabilistic model to define a novel clustering cost function that is based on maximizing the mutual information between consecutively visited clusters of states of the Markov chain defined  ...  This view forms a probabilistic interpretation of spectral clustering methods.  ...  Relations to Other Information-Theory based Clustering Problems The standard setup in information-theoretic clustering approaches is based on a given joint distribution of objects and features denoted  ... 
doi:10.1016/j.neucom.2015.12.025 fatcat:sf5pnf47ove7rlzfa6vifrceuy

A Survey on Feature Selection

Jianyu Miao, Lingfeng Niu
2016 Procedia Computer Science  
The experimental results show that unsupervised feature selection algorithms benefits machine learning tasks improving the performance of clustering.  ...  Recently, researchers from computer vision, text mining and so on have proposed a variety of feature selection algorithms and in terms of theory and experiment, show the effectiveness of their works.  ...  in the SPFS framework MCFS [8]: Features are selected based on spectral analysis and sparse regression problem UDFS [10]: Features are selected by a joint framework of discriminative analysis and 2,1  ... 
doi:10.1016/j.procs.2016.07.111 fatcat:qlq3nj66tzfs3gg4qjh7wgnwri
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