Kernelized Multiview Subspace Analysis By Self-Weighted Learning

Kernelized Multiview Projection

Conventional vision algorithms adopt a single type of feature or a simple concatenation of multiple features, which is always represented in a high-dimensional space. In this paper, we propose a novel unsupervised spectral embedding algorithm called Kernelized Multiview Projection (KMP) to better fuse and embed different feature representations. Computing the kernel matrices from different feat...

Weighted Incremental Subspace Learning

In a cognitive vision system, learning is expected to be a continuous process, which treats input images and pixels selectively. In this paper we present a method for subspace learning, which takes these considerations into account. First, we present a generalized PCA approach, which estimates the principal subspace considering weighted pixels and images. Next, we propose a method for increment...

Self-weighted Multiview Clustering with Multiple Graphs

In multiview learning, it is essential to assign a reasonable weight to each view according to the view importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we propose to explore a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each ...

Kernelized View Adaptive Subspace Learning for Person Re-identification

Qin Zhou12 [email protected] Shibao Zheng12 [email protected] Hang Su34 [email protected] Hua Yang12 [email protected] Yu Wang12 [email protected] Shuang Wu12 [email protected] 1 Department of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University 2 Shanghai Key Lab of Digital Media Processing and Transmission 3 Department of Computer Science and Technology, ...

Kernelized Subspace Ranking for Saliency Detection