d**y 发帖数: 4 | 1 什么是Compressive Sensing?
做什么的,前景如何? | O********9 发帖数: 59 | 2 The data model of compressive sensing is y=A*x+e. y is an M-dimensional
vector of measurements, A is an M by N matrix, x is an N-dimensional vector
of signal and e the noise vector. There is only a limited number of
measurements, meaning M
signal x from y by making use of the fast that x is a sparse vector.
If M>N, then one can estimate x by maximum-likelihood (equivalent to least-
squares method). However, when M
【在 d**y 的大作中提到】 : 什么是Compressive Sensing? : 做什么的,前景如何?
| O********9 发帖数: 59 | 3 The data model of compressive sensing is y=A*x+e. y is an M-dimensional
vector of measurements, A is an M by N matrix, x is an N-dimensional vector
of signal and e the noise vector. There is only a limited number of
measurements, meaning M
signal x from y by making use of the fast that x is a sparse vector.
If M>N, then one can estimate x by maximum-likelihood (equivalent to least-
squares method). However, when M
【在 d**y 的大作中提到】 : 什么是Compressive Sensing? : 做什么的,前景如何?
| d**y 发帖数: 4 | 4 thanks!
vector
the
So
Pursuit
【在 O********9 的大作中提到】 : The data model of compressive sensing is y=A*x+e. y is an M-dimensional : vector of measurements, A is an M by N matrix, x is an N-dimensional vector : of signal and e the noise vector. There is only a limited number of : measurements, meaning M: signal x from y by making use of the fast that x is a sparse vector. : If M>N, then one can estimate x by maximum-likelihood (equivalent to least- : squares method). However, when M
| s*******g 发帖数: 483 | 5 it is closely related to sparse coding, basis persuit, LASSO, which is very
hot nowadays in machine learning | R********n 发帖数: 519 | 6 呵呵,Tao等人的工作之后,所有相关的community,都开始用不同的响应速度来靠这棵
大树,从Stat.,Signal Processing, Coding theory,Information theory,Image
Processing, Computer Vision,Machine Learning,Communication。。。
这个确实是好的工作,但后面很多靠着的都是有牵强附会的嫌疑~~
very
【在 s*******g 的大作中提到】 : it is closely related to sparse coding, basis persuit, LASSO, which is very : hot nowadays in machine learning
|
|