Kernelized Convex Hull based Collaborative Representation for Tumor Classification

Background: Reliable and precise classification methods for tumor types have started to see wide deployment, in particular in the area of cancer diagnosis and personalized cancer drug design. The traditional Sparse Representation-based Classification (SRC) method can achieve high accuracy for tumor classification but also suffer from inefficiency when handling noisy datasets. To resist such disadvantage, some researchers proposed collaborative Representation-based Classification (CRC) method, which is more efficient and less complex. Method: In this paper, we design a novel Kernelized Convex Hull Collaborative Representation and Classification (KCHCRC) approach to further improve it. Though modeling the testing sample as a special convex hull with a single element, the convex hull can collaboratively be represented over the whole training samples. When the represented coefficients are fixed, we can calculate the distance between the testing sample and training samples with identical type for each category. To demonstrate the performance of our approach, we compare with the prior state-of-the-art tumor classification methods on various 11 tumor gene expression datasets. Result: The experimental results show that our approach is efficacy and efficiency.