When data are organized in matrices or arrays of higher dimensions (tensors), classical regression methods first transform these data into vectors, therefore ignoring the underlying structure of the data and increasing the dimensionality of the problem. This flattening operation typically leads to overfitting when only few training data is available. In this paper, we present a mixture-of-experts model that exploits tensorial representations for regression of tensor-valued data. The proposed formulation takes into account the underlying structure of the data and remains efficient when few training data are available. Evaluation on artificially generated data, as well as offline and real-time experiments recognizing hand movements from tactile myography prove the effectiveness of the proposed approach.
翻译:当数据以较高维度的矩阵或阵列(密度)组织起来时,古典回归法首先将这些数据转化为矢量,因此忽视了数据的基本结构,增加了问题的维度。这种平坦操作通常导致在只有很少的培训数据可用时过度匹配。在本文中,我们提出了一个专家混合模型,利用有价数据回归的极端表示法。拟议公式考虑到数据的基本结构,在几乎没有培训数据可用的情况下仍然有效。对人工生成的数据的评价,以及确认手从触觉记忆学中手动移动的离线和实时实验,证明了拟议方法的有效性。