Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors

Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost. However, singular vectors produced by SVD have been seldom used in convolutional neural networks (CNNs). This is because the inherent properties of singular vectors such as sign ambiguity and manifold features make CNNs...