Robust Structured Subspace Learning for Data Representation

By Zechao Li, J. Liu, Jinhui Tang, Hanqing Lu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

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Deep Collaborative Embedding for Social Image Understanding (2019, background): “tags and add the relevant tags through estimating the relevance between social images and tags [11], [28], [29].”
Real-Time RGB-D Activity Prediction by Soft Regression (2016, methodology): “The L1,2 norm is used to measure the regression loss because it is robust to noise and outliers [40].”
Deep rule-based classifier with human-level performance and characteristics (2018, background): “One may further consider to refine the commonly used visual features into more informative representations by uncovering an appropriate latent subspace [30].”
Robust Sparse Linear Discriminant Analysis (2019, methodology): “proposed a novel robust structured subspace learning (RSSL) method to achieve an appropriate latent subspace for data representation where l2,1 norm is adopted in the formulations of loss function and regularization term to make algorithm robust to the outliers and noise and to select discriminative”