Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103211
Title: Multi-feature spectral clustering with minimax optimization
Authors: Wang, Hongxing
Weng, Chaoqun
Yuan, Junsong
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2014
Source: Wong, H., Weng, C., & Yuan, J. (2014). Multi-feature spectral clustering with minimax optimization. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Abstract: In this paper, we propose a novel formulation for multi-feature clustering using minimax optimization. To find a consensus clustering result that is agreeable to all feature modalities, our objective is to find a universal feature embedding, which not only fits each individual feature modality well, but also unifies different feature modalities by minimizing their pairwise disagreements. The loss function consists of both (1) unary embedding cost for each modality, and (2) pairwise disagreement cost for each pair of modalities, with weighting parameters automatically selected to maximize the loss. By performing minimax optimization, we can minimize the loss for the worst case with maximum disagreements, thus can better reconcile different feature modalities. To solve the minimax optimization, an iterative solution is proposed to update the universal embedding, individual embedding, and fusion weights, separately. Our minimax optimization has only one global parameter. The superior results on various multi-feature clustering tasks validate the effectiveness of our approach when compared with the state-of-the-art methods.
URI: https://hdl.handle.net/10356/103211
http://hdl.handle.net/10220/25753
DOI: http://dx.doi.org/10.1109/CVPR.2014.523
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/CVPR.2014.523].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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