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
조회수
103
본 논문에서는 독립된 일반 부분공간의 클러스터링 문제를 고려합니다. 즉, 주어진 데이터 포인트가 독립적인 저차원 선형 부분 공간의 합집합 근처 또는 위에 있는 경우, 우리는 부분 공간을 복구하고 각 데이터 포인트에 해당 레이블을 할당하는 것을 목표로 합니다. 이 문제를 해결하기 위해 우리는 그리디 전략과 에너지 최소화 전략을 모두 활용하여 다음과 같은 가정을 기반으로 간단하면서도 효과적인 알고리즘을 제안합니다. m-분지형(즉, 완벽함) m-ary) 수집하여 구성된 트리 m- 각 노드의 가장 가까운 이웃 지점은 거의 정확한 부분 공간을 포함할 확률이 높습니다. 구체적으로, 처음에는 부분공간 후보가 여러 개로 열거됩니다. m-가지가 있는 나무. 각 트리는 데이터 포인트로 시작하여 너비 우선 검색 순서로 가장 가까운 이웃을 수집하여 성장합니다. 그런 다음 에너지 최소화 알고리즘을 초기화하기 위해 열거형에서 부분 공간 제안을 추가로 선택합니다. 결국 제안과 라벨링 결과는 반복적인 재평가와 라벨링을 통해 최종 확정됩니다. 합성 데이터와 실제 데이터를 모두 사용한 실험을 통해 제안된 방법이 최첨단 방법보다 성능이 뛰어나고 실제 적용에 실용적이라는 것을 보여줍니다.
Chao ZHANG
University of Fukui
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부
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
부
@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},}
부
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 -