However, both the graph construction and eigen-decomposition are time-consuming, and the two-stage process may deviate from directly solving the primal problem. To this end, we propose Fast Multi-view Discrete Clustering (FMDC) with anchor graphs, focusing on directly solving the spectral ...
, we propose a novel method, i.e., fast anchor-based graph-regularized low-rank representation (FA-GLRR) approach for large-scale subspace clustering... L Fan,G Lu,G Tang,... - 《Machine Vision & Applications》 被引量: 0发表: 2024年 Anchor-based multi-view subspace clustering with hi...
Based on Assumption 2, true target contigs in an organelle-sufficient assembly graph should occur in one connected component. Thus, for a real organelle-sufficient assembly graph, GetOrganelle retains the connected component with the most target-anchor contigs and deletes other such connected ...
Stair detection is a typical multitask of classification and regression. Our loss function inherits the multitask loss idea used for most object detection tasks. The loss function includes a classification loss and a location loss. The specific formula is as follows: \begin{aligned} \begin{aligned...
Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems comp...
3.3.1. Multioriented detection a) Anchor box regression and the loss function During the training process, each anchor box is described by a five-parameter representation. To accommodate the properties of smoke in multiorientation detection, a regression approach is used to determine the location of...
To solve this problem, we propose a novel method, i.e., fast anchor-based graph-regularized low-rank representation (FA-GLRR) approach for large-scale subspace clustering. Specifically, anchor graph is first used to accelerate the construction of similarity matrix, and then, some equivalent ...
Fast algorithm for anchor graph hashingdoi:10.14778/3447689.3447696Yasuhiro FujiwaraSekitoshi KanaiYasutoshi IdaAtsutoshi KumagaiNaonori UedaVLDB EndowmentPUB4722Very Large Data Bases
In this letter, we propose a novel approach, called fast semi-supervised learning with anchor graph (FSSLAG) to solve the large HSI classification problem. In the proposed FSSLAG algorithm, the anchor graph, which is parameter-free, naturally sparse and scale invariant, is first constructed. ...
Firstly, an anchor graph is constructed by means of a parameter-free adaptive neighbor assignment strategy. Meanwhile, an approximate nearest neighbor search technique is introduced to speed up the anchor graph construction. The ~(2,1)-norm regularization is then performed to select more valuable ...