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A modified neutral point method for kernel-based fusion of pattern-recognition modalities with incomplete data sets

Panov, M, Tatarchuk, A, Mottl, V and Windridge, D (2011) A modified neutral point method for kernel-based fusion of pattern-recognition modalities with incomplete data sets Lecture Notes in Computer Science, 6713. pp. 126-136.

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It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term ”neutral point” stands for the locus of points defined by the ”neutral hyperplane” in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Date : 2011
Identification Number : 10.1007/978-3-642-21557-5_15
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 14 Jun 2012 11:17
Last Modified : 31 Oct 2017 14:19

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