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May 26, 2020 · This paper attempts to take advantage of both dual linear regression and sparse coding for set-to-set based object recognition.
ABSTRACT. This paper attempts to take advantage of both dual linear regression and sparse coding for set-to-set based object recognition.
This paper proposes a novel method, called robust latent subspace learning (RLSL), for image classification. We formulate an RLSL problem as a joint ...
In this study, we consider the problem of subspace clustering in the presence of spatially contiguous noise, occlusion, and disguise.
We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the ...
Missing: Retrieval. | Show results with:Retrieval.
Dec 7, 2022 · To perform such analysis, we represent a single image or text as low-dimensional linear subspaces and per- form retrieval based on subspace ...
The results show that our method is efficient in recovering the low-rank face subspaces by re- moving the noise in the training images, thus significantly.
In this paper, we propose a method to discover low-dimensional linear subspace from a set of data containing both inliers and a significant amount of ...
Jan 14, 2023 · We propose the Grassmannian learning mutual subspace method (G-LMSM), a NN layer embedded on top of CNNs that can process image sets more effectively.
MMP aims at maximizing the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction ...
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