Graph embeddingMultiple kernel learningStructured sparsityMany real-world datasets are represented by multiple features or modalities which often provide compatible and complementary information to each other. In order to obtain a good data representation that synthesizes multiple features, researchers have ...
link:OrthoReg: Improving Graph-regularized MLPs via Orthogonality... Motivation GNN在infer的时候时延比较大,因此很多工作考虑将GNN蒸馏为MLP 然而MLP缺乏节点的交互,常见的正则化方法(GR-MLP)方法都是遵从 同配性假设,也就是相连的节点表征相似。然而,作者发现这种正则化可能会带dimensional collapse problem,进而影...
Graph regularized non-negative matrix factorization by maximizing correntropy. arXiv preprint arXiv:1405.2246, 2014.Li, L., Yang, J., Zhao, K., Xu, Y., Zhang, H., Fan, Z.: Graph regularized non-negative matrix factorization by maximizing correntropy. arXiv preprint arXiv:1405.2246 (2014)...
We proposed a new low-rank subspace learning algorithm, termed latent graph-regularized inductive robust principal component analysis (LGIRPCA), in this paper. Different from the existing low-rank subspace learning methods, LGIRPCA considers the feature manifold structure of a given data set and ...
In this paper, we present a graph regularized GM-pLSA (GRGM-pLSA) model as an extension of GM-pLSA to embed this term correlation information into the process of model learning. Specifically, grounded on the manifold regularization principle, a graph regularizer is introduced to characterize the...
Next, we briefly introduce the sparse graph-regularized dictionary learning (SGRDL) regularizer for solving the FWI problem. Then, the propose SGRDL-FWI method and share the corresponding numerical algorithm. Finally, we present series of experiments that demonstrate the superiority of our SGRDL-FWI...
Paper tables with annotated results for G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The official pytorch implementation of "GRACE: Graph-Regularized Attentive Convolutional Entanglement with Laplacian Smoothing for Robust DeepFake Video Detection". Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2024).[...
Graph regularized Non-negative Matrix Factorization (GNMF) algorithm which avoids this limitation by incorporating a geometrically based regularizer. 3.1 The Objective Function Recall that NMF tries to find a basis that is optimized for the linear approximation of the data which are drawn according ...
In this paper, we propose a novel joint graph regularized dictionary learning and sparse ranking method for multi-modal multi-shot person Re-ID. First, we explore the probe-based geometrical structure by enforcing the smoothness between the codings/coefficients, which refers to the multi-shot ...