Fisher-like Kernels Using Fuzzy c-Means
| Accession number;06A0843646 |
| Title;Fisher-like Kernels Using Fuzzy c-Means |
| Author;INOKUCHI RYO(Univ. Tsukuba, Graduate School of System and Information Engineering, JPN) MIYAMOTO SADAAKI(Univ. Tsukuba, Graduate School of System and Information Engineering, JPN) |
Journal Title;Faji Shisutemu Shinpojiumu Koen Ronbunshu (CD-ROM)
|
Journal Code:L0486B
|
ISSN:1341-9080
|
|
VOL.22nd;NO.;PAGE.6C4-4(2006)
|
| Figure&Table&Reference;FIG.4, REF.11 |
| Pub. Country;Japan |
| Language;Japanese |
| Abstract;The Fisher kernel, which refers to the inner product in the feature space of the Fisher score, has been known to be a successful tool for feature extraction using a probabilistic model. If an appropriate probabilistic model for given data is known, the Fisher kernel provides a discriminative classifier with good generalization. However, if the distribution is unknown, it is difficult to obtain an appropriate Fisher kernel. In this paper, we propose a new nonparametric Fisher-like kernel derived from fuzzy clustering instead of a probabilistic model, noting that fuzzy clustering methods such as a family of fuzzy c-means are highly related to probabilistic models. Numerical examples show the effectiveness of the proposed method. (author abst.) |
|
