Large graph embeddingbalanced $K$ K -means based hierarchical $K$ K -meansanchor based graphdimensionality reductionThere are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), ...
Ensemble learningImage clusteringDimension reductionManifold learningManifold learning has attracted more and more attention in machine learning for past decades. Unsupervised Large Graph Embedding (ULGE), which performs well on the large-scale data, has been proposed......
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. H
However, in the unsupervised graph embedding methods, the graph is not considered as an optimized graph since there is no label that can be used to construct this graph. In addition, the running of traditional graph embedding methods becomes very time-consuming on large-scale datasets due to ...
the diffusion process once. The unconditional latent may be calculated using an unconditioned text embedding. Because implementations may only do one pass through the diffusion model, implementations may set the time-step variable t (used by the diffusion model170) to a large value, e.g., t=...
In this paper, we study the unsupervised multi-view graph embedding (UMGE) prob- lem, which aims to learn graph embedding from multiple perspectives in an unsupervised manner. However, the vast majority of multi- view learning work focuses on non-graph data, and surprisingly there are limited...
graph distance of zero. During training, we fixed the graph layer and only optimized the prediction layer. We observe a large dependency between the prediction loss of a candidate graph and the corresponding graph loss (Fig.5). We conclude that the hypothesis that graphs that are closer to ...
Large graph construction for scalable semi-supervised learning From point to set: Extend the learning of distance metrics 【待阅读】 (4)top-k count 标签估计: 如果两个视频序列属于同一个行人,那么它们在不同的衡量维度上需要非常接近。具体来说,如果未标签序列 xi属于行人 ...
Ioannidis VN, Berberidis D, Giannakis GB (2019) Graphsac: Detecting anomalies in large-scale graphs. arXiv: Learning Kingma DP, Ba J (2015) A method for stochastic optimization. In: ICLR Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: ICLR Ki...
Efficient Supervised Graph Embedding Hashing for large-scale cross-media retrieval 2024, Pattern Recognition Citation Excerpt : Inspired by this, many hashing methods have been proposed to perform large-scale retrieval task in recent years [4–7]. However, most of them are restricted to apply on ...