Journal ArticleDOI
Laplacian Regularized Low-Rank Representation and Its Applications
Ming Yin,Junbin Gao,Zhouchen Lin +2 more
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TLDR
The proposed general Laplacian regularized low-rank representation framework for data representation takes advantage of the graph regularizer and can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data.Abstract:
Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition, computer vision and signal processing. In the real world, data often reside on low-dimensional manifolds embedded in a high-dimensional ambient space. However, the LRR method does not take into account the non-linear geometric structures within data, thus the locality and similarity information among data may be missing in the learning process. To improve LRR in this regard, we propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR). By taking advantage of the graph regularizer, our proposed method not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data. The extensive experimental results on image clustering, semi-supervised image classification and dimensionality reduction tasks demonstrate the effectiveness of the proposed method.read more
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Journal ArticleDOI
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
TL;DR: A comprehensive review of the literature in graph embedding can be found in this paper, where the authors introduce the formal definition of graph embeddings as well as the related concepts.
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A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
TL;DR: This survey conducts a comprehensive review of the literature in graph embedding and proposes two taxonomies ofGraph embedding which correspond to what challenges exist in differentgraph embedding problem settings and how the existing work addresses these challenges in their solutions.
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Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization
TL;DR: An iterative multiview agreement strategy by minimizing the divergence objective among all factorized latent data-cluster representations during each iteration of optimization process, where such latent representation from each view serves to regulate those from other views, and such an intuitive process iteratively coordinates all views to be agreeable.
Proceedings ArticleDOI
Deep Transfer Learning for Person Re-Identification
TL;DR: A two-stepped fine-tuning strategy with proxy classifier learning is developed to transfer knowledge from auxiliary datasets to address the training data sparsity problem from the supervised and unsupervised settings.
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Deep Transfer Learning for Person Re-identification
TL;DR: Zhang et al. as discussed by the authors proposed a number of deep transfer learning models to address the data sparsity problem and achieved state-of-the-art performance in person re-ID.
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