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- Yong Hai YuSuggested from co-authorship
- Shuicheng YanSuggested from co-authorship
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Works150 from public data
- Robust Recovery of Subspace Structures by Low-Rank Representation3,594
It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, it is proved that under certain conditions LRR can exactly recover the row space of the original data.
- Robust Subspace Segmentation by Low-Rank Representation1,756
Both theoretical and experimental results show that low-rank representation is a promising tool for subspace segmentation from corrupted data.
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- Spatio-temporal convolutional features with nested LSTM for facial expression recognition161
A novel end-to-end architecture termed Spatio-Temporal Convolutional features with Nested LSTM (STC-NLSTM), which learns the muti-level appearance features and temporal dynamics of facial expressions in a joint fashion is proposed.
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- Active Subspace: Toward Scalable Low-Rank Learning86
This work revisits the classic mechanism of low-rank matrix factorization, based on which an active subspace algorithm is presented for efficiently solving nuclear norm regularized optimization problems by transforming large-scale NNROPs into small-scale problems.
- Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation82
A joint label prediction based Robust Semi-Supervised Adaptive Concept Factorization (RS2ACF) framework that incorporates the joint neighborhood reconstruction error over the new representations and predicted labels of both labeled and unlabeled data, so the manifold structures can be preserved explicitly and adaptively in the representation space and label space at the same time.
- Deeper cascaded peak-piloted network for weak expression recognition80
Experimental results on two popular facial expression databases, CK+$$+ and Oulu-CASIA, show the superiority of the proposed Deeper Cascaded Peak-piloted Network over state-of-the-art methods.
- Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit74
It is mathematically proved that LRR can weaken the dependence on the row-coherence, provided that the dictionary is well-conditioned and has a rank of not too high, which provides some elementary principles for dictionary learning and naturally lead to a practical algorithm for recovering mixture data.
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- Learning image compressed sensing with sub-pixel convolutional generative adversarial network69
The proposed novel sub-pixel convolutional generative adversarial network (GAN) to learn compressed sensing reconstruction of images is superior to some state-of-the-art deep learning based and iterative optimization based algorithms, in terms of both time complexity and reconstruction quality.
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- Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning59
This work proposes a novel structured discriminative block-diagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification and incorporates a locality constraint of atoms into the DL procedures to keep local information and obtain the codes of samples over each class separately.
- Twin-Incoherent Self-Expressive Locality-Adaptive Latent Dictionary Pair Learning for Classification56
- Robust Subspace Clustering With Compressed Data54
The proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions, and is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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