The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
pandangan teks lengkap
101
Dalam makalah ini, kami mempertimbangkan masalah pengelompokan subruang umum bebas. Iaitu, dengan titik data yang diberikan terletak berhampiran atau pada penyatuan subruang linear dimensi rendah bebas, kami berhasrat untuk memulihkan subruang dan menetapkan label yang sepadan kepada setiap titik data. Untuk menyelesaikan masalah ini, kami memanfaatkan kedua-dua strategi tamak dan strategi pengurangan tenaga untuk mencadangkan algoritma yang mudah tetapi berkesan berdasarkan andaian bahawa m-bercabang (iaitu, sempurna m-ary) pokok yang dibina dengan mengumpul m-titik jiran terdekat dalam setiap nod mempunyai kebarangkalian tinggi untuk mengandungi subruang yang hampir tepat. Khususnya, pada mulanya, calon subruang dikira dengan berbilang m-pokok bercabang. Setiap pokok bermula dengan titik data dan berkembang dengan mengumpulkan jiran terdekat dalam susunan carian pertama luas. Kemudian, cadangan subruang dipilih lagi daripada penghitungan untuk memulakan algoritma pengecilan tenaga. Akhirnya, kedua-dua cadangan dan hasil pelabelan dimuktamadkan melalui anggaran semula dan pelabelan berulang. Eksperimen dengan kedua-dua data sintetik dan dunia sebenar menunjukkan bahawa kaedah yang dicadangkan boleh mengatasi kaedah terkini dan praktikal dalam aplikasi sebenar.
Chao ZHANG
University of Fukui
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Salinan
Chao ZHANG, "Energy Minimization over m-Branched Enumeration for Generalized Linear Subspace Clustering" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 12, pp. 2485-2492, December 2019, doi: 10.1587/transinf.2019EDP7138.
Abstract: In this paper, we consider the clustering problem of independent general subspaces. That is, with given data points lay near or on the union of independent low-dimensional linear subspaces, we aim to recover the subspaces and assign the corresponding label to each data point. To settle this problem, we take advantages of both greedy strategy and energy minimization strategy to propose a simple yet effective algorithm based on the assumption that an m-branched (i.e., perfect m-ary) tree which is constructed by collecting m-nearest neighbor points in each node has a high probability of containing the near-exact subspace. Specifically, at first, subspace candidates are enumerated by multiple m-branched trees. Each tree starts with a data point and grows by collecting nearest neighbors in the breadth-first search order. Then, subspace proposals are further selected from the enumeration to initialize the energy minimization algorithm. Eventually, both the proposals and the labeling result are finalized by iterative re-estimation and labeling. Experiments with both synthetic and real-world data show that the proposed method can outperform state-of-the-art methods and is practical in real application.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7138/_p
Salinan
@ARTICLE{e102-d_12_2485,
author={Chao ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Energy Minimization over m-Branched Enumeration for Generalized Linear Subspace Clustering},
year={2019},
volume={E102-D},
number={12},
pages={2485-2492},
abstract={In this paper, we consider the clustering problem of independent general subspaces. That is, with given data points lay near or on the union of independent low-dimensional linear subspaces, we aim to recover the subspaces and assign the corresponding label to each data point. To settle this problem, we take advantages of both greedy strategy and energy minimization strategy to propose a simple yet effective algorithm based on the assumption that an m-branched (i.e., perfect m-ary) tree which is constructed by collecting m-nearest neighbor points in each node has a high probability of containing the near-exact subspace. Specifically, at first, subspace candidates are enumerated by multiple m-branched trees. Each tree starts with a data point and grows by collecting nearest neighbors in the breadth-first search order. Then, subspace proposals are further selected from the enumeration to initialize the energy minimization algorithm. Eventually, both the proposals and the labeling result are finalized by iterative re-estimation and labeling. Experiments with both synthetic and real-world data show that the proposed method can outperform state-of-the-art methods and is practical in real application.},
keywords={},
doi={10.1587/transinf.2019EDP7138},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Energy Minimization over m-Branched Enumeration for Generalized Linear Subspace Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 2485
EP - 2492
AU - Chao ZHANG
PY - 2019
DO - 10.1587/transinf.2019EDP7138
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2019
AB - In this paper, we consider the clustering problem of independent general subspaces. That is, with given data points lay near or on the union of independent low-dimensional linear subspaces, we aim to recover the subspaces and assign the corresponding label to each data point. To settle this problem, we take advantages of both greedy strategy and energy minimization strategy to propose a simple yet effective algorithm based on the assumption that an m-branched (i.e., perfect m-ary) tree which is constructed by collecting m-nearest neighbor points in each node has a high probability of containing the near-exact subspace. Specifically, at first, subspace candidates are enumerated by multiple m-branched trees. Each tree starts with a data point and grows by collecting nearest neighbors in the breadth-first search order. Then, subspace proposals are further selected from the enumeration to initialize the energy minimization algorithm. Eventually, both the proposals and the labeling result are finalized by iterative re-estimation and labeling. Experiments with both synthetic and real-world data show that the proposed method can outperform state-of-the-art methods and is practical in real application.
ER -