@inproceedings{e16729f01589464a862dd9f48ad4298a,
title = "Iterative deep subspace clustering",
abstract = "Recently, deep learning has been widely used for subspace clustering problem due to the excellent feature extraction ability of deep neural network. Most of the existing methods are built upon the auto-encoder networks. In this paper, we propose an iterative framework for unsupervised deep subspace clustering. In our method, we first cluster the given data to update the subspace ids, and then update the representation parameters of a Convolutional Neural Network (CNN) with the clustering result. By iterating the two steps, we can obtain not only a good representation for the given data, but also more precise subspace clustering result. Experiments on both synthetic and real-world data show that our method outperforms the state-of-the-art on subspace clustering accuracy.",
keywords = "Convolutional Neural Network, Subspace clustering, Unsupervised deep learning",
author = "Lei Zhou and Shuai Wang and Xiao Bai and Jun Zhou and Edwin Hancock",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018 ; Conference date: 17-08-2018 Through 19-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97785-0\_5",
language = "英语",
isbn = "9783319977843",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "42--51",
editor = "Hancock, \{Edwin R.\} and Ho, \{Tin Kam\} and Battista Biggio and Wilson, \{Richard C.\} and Antonio Robles-Kelly and Xiao Bai",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Proceedings",
address = "德国",
}