In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional connectivity data for brain diseases. This model is an end-to-end clustering algorithm that can learn potentially advanced features and cluster disea...
Kernel representationSemantic representationIn this paper, we propose a novel deep clustering framework via dual-supervised multi-kernel mapping, namely DCDMK, to improve clustering performance by learning linearly structural separable data representations. In the DCDMK framework, we introduce a kernel-aid...
[16] systematically explored deep clustering from a network structure perspective but did not delve into clustering algorithms involving graph neural networks. Ren et al. [17] examined deep clustering from the perspective of data sources, categorizing it into single-view, semi-supervised, multi-view...
Low-Rank Kernel Tensor Learning for Incomplete Multi-View ClusteringLRKT-IMVCAAAI 2024- SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View ClusteringSURERAAAI 2024- A Non-parametric Graph Clustering Framework for Multi-View DataNpGCAAAI 2024- ...
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Technological advances have made it possible to study a patient from multiple angles with high-dimensional, high-throughput multiscale biomedical data. In oncology, massive amounts of data are being generated, ranging from molecular, histopathology, radi
Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large-scale datasets. In this work, we present
Class-clustering index of network response To visualize the network responses to the similarity-controlled stimulus set, a principal component analysis78 was used for dimension reduction. By minimizing the difference between the original and low-dimensional distributions of neighbor distances, a 2D represe...
The range for the kernel length was 20/15, 16/12 and 12/8. The fully connected part consists of 3 layers with 120/60/25 nodes, while the range of tested values was 140/80/40, 120/60/25 and 100/60/20. We selected leaky ReLU as the activation function of all layers followed by ...
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...