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
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