The paper proposes a classification algorithm based on support tensor machines which finds the maximum margin between the tensor spaces. The proposed algorithm has been deployed to classify erythemato-squamous diseases (ESDs) with the help of its features. Features are derived from the skin lesion images of ESDs, and it has been represented as second-order tensors, i.e., \( \varvec{X} \in \varvec{ }{\mathbb{R}}^{\varvec{n}} \) can be transformed into \( \varvec{X} \in \,\varvec{ }{\mathbf{\Re }}^{{\varvec{n}_{1} }} \,\varvec{ } \otimes \,{\mathbf{\Re }}^{{\varvec{n}_{2} }} \) where \( n_{1} \times n_{2} \cong n \). After deriving the features from the skin lesion images, dominant features are extracted using Tucker tensor decomposition method. Most of the existing machine learning algorithms depend on the vector-based learning models, and these algorithms suffer from the data overfitting problem. To resolve this problem, in this paper, tensor-based learning is implemented for classification. Proposed algorithm is evaluated with the real-time dataset (Xie et al. in: He, Liu, Krupinski, Xu (eds) Health information science, Springer, Berlin, 2012), and higher classification accuracy of 99.93–100% is achieved. The acquired results are compared with the existing machine learning algorithms, and it drives home the point that the proposed algorithm provides higher classification accuracy.

中文翻译：

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基于张量学习的红斑鳞状疾病的分类和预测

提出了一种基于支持张量机的分类算法，该算法可以找到张量空间之间的最大余量。借助该算法的功能，已将提出的算法用于对红斑鳞状疾病（ESD）进行分类。特征来自ESD的皮肤病变图像，并且已表示为二阶张量，即\（\ varvec {X} \ in \ varvec {} {\ mathbb {R}} ^ {\ varvec {n }} \）可以转换为\（\ varvec {X} \ in \，\ varvec {} {\ mathbf {\ Re}} ^ {{\ varvec {n} _ {1}}} \，\ varvec { } \ otimes \，{\ mathbf {\ Re}} ^ {{\ varvec {n} _ {2}}} \）其中\（n_ {1} \ times n_ {2} \ cong n \）。从皮肤病变图像中提取特征后，使用塔克张量分解法提取主要特征。现有的大多数机器学习算法都依赖于基于矢量的学习模型，并且这些算法存在数据过拟合的问题。为了解决这个问题，本文采用基于张量的学习进行分类。提出的算法通过实时数据集进行评估（Xie等人：He，Liu，Krupinski，Xu（eds）健康信息科学，Springer，柏林，2012年），分类精度更高，达到了99.93–100％。将获得的结果与现有的机器学习算法进行比较，并证明了该算法提供了更高的分类精度。