Scalar Quantization as Sparse Least Square Optimization
نویسندگان
چکیده
منابع مشابه
Vector Quantization as Sparse Least Square Optimization
Vector quantization aims to form new vectors/matrices with shared values close to the original. It could compress data with acceptable information loss, and could be of great usefulness in areas like Image Processing, Pattern Recognition and Machine Learning. In recent years, the importance of quantization has been soaring as it has been discovered huge potentials in deploying practical neural ...
متن کاملSpiral MRI Reconstruction Using Least Square Quantization Table
Recently, the authors introduced least squares quantization table (LSQT) method to accelerate the direct Fourier transform to reconstruct magnetic resonance images acquired using a spiral trajectory. In this paper, we will discuss the LSQT further in its adaptability, reusability and choice of the number of groups. The experimental results show that the LSQT method has better adaptability for t...
متن کاملFast Least Square Matching
Least square matching (LSM) is one of the most accurate image matching methods in photogrammetry and remote sensing. The main disadvantage of the LSM is its high computational complexity due to large size of observation equations. To address this problem, in this paper a novel method, called fast least square matching (FLSM) is being presented. The main idea of the proposed FLSM is decreasing t...
متن کاملLeast squares solution of nearly square overdetermined sparse linear systems
The solution of nearly square overdetermined linear systems is studied. The sparse QR technique is compared with two sparse LU-based techniques. Numerical tests with realworld and artificial matrices indicate that the LU techniques are more accurate for incompatible right-hand sides. Also, the amount of floating point operations required by LU techniques is approximately one half smaller than Q...
متن کاملSparse Optimization with Least-Squares Constraints
The use of convex optimization for the recovery of sparse signals from incomplete or compressed data is now common practice. Motivated by the success of basis pursuit in recovering sparse vectors, new formulations have been proposed that take advantage of different types of sparsity. In this paper we propose an efficient algorithm for solving a general class of sparsifying formulations. For sev...
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 Pattern Analysis and Machine Intelligence
سال: 2021
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2019.2952096