.png){kind=icon}
Return
Structure preserved fast dimensionality reduction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.111817.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Many graph-based unsupervised dimensionality reduction techniques have raised concerns about their high accuracy. However, there is an urgent need to address the enormous time consumption problem in large-scale data scenarios. Therefore, we present a novel approach named Structure Preserved Fast Dimensionality Reduction (SPFDR). Firstly, the parameter-insensitive, sparse, and scalable bipartite graph is constructed to build the similarity matrix. Then, employing alternating iterative optimization, the linear dimensionality reduction matrix and the optimal similarity matrix preserved cluster structure are learned. The computational complexity of the conventional graph-based dimension reduction method costs O(n(2)d+d(3)), yet the proposed approach is O(ndm+nm(2)), wherein n, m, and d are the number of instances, anchors, and features, respectively. Eventually, experiments conducted with multiple open datasets will provide convincing evidence for how effective and efficient the proposed method is.
Keywords:
Dimensionality reduction
Unsupervised learning
Bipartite graph
Large-scale data
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
Cited Papers
Joint graph optimization and projection learning for dimensionality reduction.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSave
Dimensionality reduction on Anchorgraph with an efficient Locality Preserving ProjectionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSave
View more