TY - JOUR
T1 - Multi-view unsupervised feature selection with adaptive similarity and view weight
AU - Hou, Chenping
AU - Nie, Feiping
AU - Tao, Hong
AU - Yi, Dongyun
N1 - Publisher Copyright:
© 1989-2012 IEEE.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse ℓ2,p-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.
AB - With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse ℓ2,p-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.
KW - adaptive similarity and view weight
KW - multiple view data mining
KW - sports action recognition
KW - unsupervised feature selection
UR - http://www.scopus.com/inward/record.url?scp=85029389131&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2017.2681670
DO - 10.1109/TKDE.2017.2681670
M3 - 文章
AN - SCOPUS:85029389131
SN - 1041-4347
VL - 29
SP - 1998
EP - 2011
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 9
M1 - 7876761
ER -