.png){kind=icon}
Return
Prime Discriminant Simplicial Complex
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2012.2223825.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose the prime discriminant simplicial complex (PDSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a prime simplicial complex, we classify unlabeled samples based on the nearest projection distances from the samples to the simplicial complexes. We also extend the extrapolation ability of these complexes with a projection constraint term. Experiments in simulated and practical datasets indicate that, compared with several published algorithms, the proposed PDSC approaches achieve promising performance without losing structure representation.
Keywords:
Object recognition
persistent homology
supervised learning
topology
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
ShareSaveShareSaveShareSaveStrength and Ultrasonic Characteristics of Cemented Paste Backfill Incorporating Foaming Agent.png)
Minerals
OAAI
MineralsIF0
OAAIShareSaveShareSaveShareSaveShareSave
View more