Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes
نویسندگان
چکیده
منابع مشابه
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching
Let f : {−1, 1} → R be an n-variate polynomial consisting of 2 monomials, in which only s 2 coefficients are non-zero. The goal is to learn the polynomial by querying the values of f . We introduce an active learning framework that is associated with a low query cost and computational runtime. The significant savings are enabled by leveraging sampling strategies based on modern coding theory, s...
متن کاملLearning sparse codes
We describe a method for learning an overcomplete set of basis functions for the purpose of modeling sparse structure in images. The sparsity of the basis function coefficients is modeled with a mixture-of-Gaussians distribution. One Gaussian captures nonactive coefficients with a small-variance distribution centered at zero, while one or more other Gaussians capture active coefficients with a ...
متن کاملSparse Graph Codes for Mult Signal Shap
We present a method to combine error-correction coding and spectral-efficient modulation for transmission over the Additive White Gaussian Noise (AWGN) channel. The code employs signal shaping which can provide a so-called shaping gain. The code belongs to the family of sparse graph codes for which efficient decoding algorithms can be derived. Simulation results show that the performance of the...
متن کاملMultilevel Group Testing via Sparse-graph Codes
In this paper, we consider the problem of multilevel group testing, where the goal is to recover a set of K defective items in a set of n items by pooling groups of items and observing the result of each test. The main difference of multilevel group testing with the classical non-adaptive group testing problem is that the result of each test is an integer in the set [L] = {0, 1, · · · , L}: if ...
متن کاملStatistical Physics Methods for Sparse Graph Codes
This thesis deals with the asymptotic analysis of coding systems based on sparse graph codes. The goal of this work is to analyze the decoder performance when transmitting over a general binary-input memoryless symmetricoutput (BMS) channel. We consider the two most fundamental decoders, the optimal maximum a posteriori (MAP) decoder and the sub-optimal belief propagation (BP) decoder. The BP d...
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 Information Theory
سال: 2019
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2018.2864276