Abstract
Due to missing values, incomplete dataset is ubiquitous in multimodal scene. Complete data is a prerequisite of the most existing multimodality data fusion methods. For incomplete multimodal high-dimensional data, we propose a feature selection and classification method. Our method mainly focuses on extracting the most relevant features from the high-dimensional features and then improving the classification accuracy. The experimental results show that our method produces considerably better performance on incomplete multimodal data such as ADNI dataset and Office dataset, compared to the case of complete data.
Highlights
In the era of Internet, there are many different modalities, such as images, video, and text
For incomplete multimodal high-dimensional data, we propose a feature selection and classification method
We evaluate the performance of our method by employing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Office dataset, respectively
Summary
In the era of Internet, there are many different modalities, such as images, video, and text. The diagnoses of Alzheimer’s Disease (AD) by multimodal classification are a great example and have achieved remarkable success compared to single modal methods in multiple experiments. Liu et al [6] mentioned a multihypergraph learning (MHL) method to deal with multimodality data. This method achieved promising results in AD/MCI classification. Liu et al [8] proposed a linearized and kernelized sparse multitask learning for predicting cognitive outcomes in Alzheimer’s Disease. Li et al [9, 10] proposed a multitask deep learning method for diagnosing Alzheimer’s Disease by combining MRI, PET, and Assessment Scale-Cognitive subscale (ADASCog) with the restricted Boltzmann machine. Liu et al [12] proposed a view-aligned hypergraph learning (VAHL) method and utilized incomplete multimodality data for AD/MCI diagnosis
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