Publikasjonsdetaljer
Tidsskrift: Lecture Notes in Computer Science (LNCS), vol. 10269 LNCS, p. 431–442, 2017
Utgivere: Springer
Internasjonale standardnumre:
Trykt: 0302-9743
Elektronisk: 1611-3349
Lenker:
ARKIV: http://hdl.handle.net/10037/13697
DOI: doi.org/10.1007/978-3-319-59126-1_36
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of Gaussian Mixture Models for incomplete data on different scales, we are able to learn a kernel for incomplete data that does not depend on any critical hyperparameters, unlike the commonly used RBF kernel. To evaluate our method, we perform experiments on two real datasets. PCKID outperforms the baseline methods for all fractions of missing values and in some cases outperforms the baseline methods with up to 25% points.