Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding

By Lefei Zhang, Qian Zhang, Liangpei Zhang, Dacheng Tao, Xin Huang, Bo Du. Pattern Recognition, 2015

302 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cebed33105d5b894696a40c10392314f914d04ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Bo Du
2. Liangpei Zhang
3. Dacheng Tao
4. Wei Dang Lu
5. Pingkun Yan
6. Lefei Zhang
7. Ying Li
8. Xia Hu

In Computer Vision and Pattern Recognition

1. Liangpei Zhang
2. Bo Du
3. Lefei Zhang
4. Dacheng Tao
5. Ying Li
6. Haokui Zhang
7. Qiang Shen
8. Xia Hu

Citations in the corpus

• Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network (2020, methodology): “Zhang proposed an ensemble manifold regularized sparse low-rank approximation (EMR-SLRA) algorithm for multiview feature embedding [14].”
• Large-Scale Heterogeneous Feature Embedding (2019, methodology): “Third, the data sparsity problem is significant (Zhang et al. 2015b), e.g., in social media, a large proportion of words or attributes often only contribute to a small number of users, providing insufficient information to accomplish effective embedding.”
• Deep learning for lung Cancer detection and classification (2020, background): “The advantage of feature selection is to determine the importance of original feature set [35, 36].”
• Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017, methodology): “Again, DR techniques may be utilized to extract low dimensional spectral–spatial features while preserving the discriminative information, such as tensor discriminative locality alignment (TDLA)-based feature extraction [26] and sparse low-rank approximation-based feature embedding [27].”
• Deeply-supervised CNN for prostate segmentation (2017, background): “Over the past decades, constructing effective feature engineering [18] has been a mainstream topic in medical image segmentation.”
• Scene Classification via a Gradient Boosting Random Convolutional Network Framework (2016, background): “” The simplest form of regularization through shrinkage is direct proportional shrinkage [29], [30].”
• Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint (2016, background): “…representative features, which contain the inherent structures of the input data, can be efficiently mined out from the complicated raw data for various data mining tasks such as multimedia retrieval [3], feature engineering [4], recommendation system [5, 6], classification [7], information…”
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, background): “, in the aspects of handcrafted feature description [65]–[68], discriminative feature learning [13], [69], [70], and powerful classifier designing [71], [72].”
• Online Metric Learning for Multi-Label Classification (2020, methodology): “validation. We use the default parameter for OSML-ELM. Performance Measurements To fairly measure the performance of our method and baseline methods, we consider the following evaluation measurements [35, 36]: Micro-F1: computes true positives, true negatives, false positives and false negatives ove”
• An Incremental Self-Labeling Strategy for Semi-Supervised Deep Learning Based on Generative Adversarial Networks (2020, methodology): “Therefore, research on techniques such as efficient feature extraction [5], [6], deep transfer learning [7], and semi-supervised deep learning (SSL) [8] are key to further promoting the effective application of deep learning.”