Matrix Product State for Higher-Order Tensor Compression and Classification
نویسندگان
چکیده
منابع مشابه
A Higher-Order Structure Tensor
Structure tensors are a common tool for orientation estimation in image processing and computer vision. We present a generalization of the traditional second-order model to a higher-order structure tensor (HOST), which is able to model more than one significant orientation, as found in corners, junctions, and multi-channel images. We provide a theoretical analysis and a number of mathematical t...
متن کاملCompression of Anisotropic Tensor-product Discretizations
This paper is concerned with the discretization and compression of integral operators. It continues the work of [19]. Based on the framework of tensor-product biorthogonal wavelet bases we construct compression schemes for operator adapted sparse grid type discretizations. These discretization and compression schemes preserve the approximation order of the standard full-grid spaces. We give det...
متن کاملHigher Order Prediction for Geometry Compression
A lot of techniques have been developed for the encoding of triangular meshes as this is a widely used representation for the description of surface models. Although methods for the encoding of the neighbor information, the connectivity, are near optimal, there is still room for better encodings of vertex locations, the geometry. Our geometry encoding strategy follows the predictive coding para...
متن کاملMatrix product state representations
This work gives a detailed investigation of matrix product state (MPS) representations for pure multipartite quantum states. We determine the freedom in representations with and without translation symmetry, derive respective canonical forms and provide efficient methods for obtaining them. Results on frustration free Hamiltonians and the generation of MPS are extended, and the use of the MPS-r...
متن کاملGeneralized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion
Low-rank tensor estimation has been frequently applied in many real-world problems. Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may become very slow or even not applicable for large-scale problems. To address this difficulty, we therefore propose an efficient and scalable core tensor Schatten 1-norm minimization method for simultaneous tensor decompositi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2017
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2017.2703882