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Modeling Heterogeneity in Feature Selection for MCI Classification
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Modeling Heterogeneity in Feature Selection for MCI Classification
Conventional methods designed for Mild Cognitive Impairment (MCI) classification usually assume that the MCI subjects are homogeneous. However, recent discoveries indicate that MCI has heterogeneous neuropathological origins which may contribute to the sub-optimal performance of conventional methods. To compensate for the limitations of existing methods, we propose Maximum Margin Heterogeneous Feature Selection (MMHFS) by explicitly considering the heterogeneous distribution of MCI data. More specifically, the proposed method simultaneously performs unsupervised clustering discovery on MCI data and conducts discriminant feature selection to help classify MCI from Normal Control (NC). It is worth noting that these two processes can benefit from each other, thus enabling the proposed method to achieve better performance.
Conventional methods designed for Mild Cognitive Impairment (MCI) classification usually assume that the MCI subjects are homogeneous. However, recent discoveries indicate that MCI has heterogeneous neuropathological origins which may contribute to the sub-optimal performance of conventional methods. To compensate for the limitations of existing methods, we propose Maximum Margin Heterogeneous Feature Selection (MMHFS) by explicitly considering the heterogeneous distribution of MCI data. More specifically, the proposed method simultaneously performs unsupervised clustering discovery on MCI data and conducts discriminant feature selection to help classify MCI from Normal Control (NC). It is worth noting that these two processes can benefit from each other, thus enabling the proposed method to achieve better performance.
