| Full text | |
| Author(s): |
Martins, Guilherme Brandao
;
Papa, Joao Paulo
;
DeCarvalho, BM
;
Goncalves, LMG
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022); v. N/A, p. 6-pg., 2022-01-01. |
| Abstract | |
In this paper, we proposed a fast and scalable unsupervised Optimum-Path Forest for improving the initialization of Gaussian mixture models. Taking advantage of Optimum-Path Forest attributes such as on-the-fly number of clusters estimation and its intrinsic non-parametric nature, we exploited the k-Approximate Nearest Neighbors graph to build its adjacency relation, enabling it not only to initialize the Expectation-Maximization algorithm but to be employed for clustering on large datasets as well. From experiments conducted on eight datasets, the results indicated the proposed approach is able to encode Gaussian parameters more naturally and intuitively compared to other clustering algorithms such as k-means. Furthermore, the proposed approach has shown great scalability, making it a viable alternative to traditional Optimum-Path Forest clustering. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |