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\begin{document}

\lecture{12 -- More compressed sensing, Oct 7, 2014}{Fall 2014}{Prof.\ Eric Price}{Tianyang Li}

\section{Overview}
In the last lecture: {\em regular compressed sensing}.

In this lecture: {\em model-based compressed sensing}.

\section{Compressed sensing}
\begin{itemize}
\item $x$ is $k$-sparse
\item observe $y=Ax+e$
\item recover $\hat{x} \approx x$ where 
	$\norm{\hat{x} - x}{2} \lesssim \norm{e}{2}$
\end{itemize}
(or $x$ is ``approximately'' $k$-sparse and we recover $\hat{x}$ where 
$\norm{\hat{x} - x}{2} \leq \norm{e}{2} 
	+ C \underset{\text{$k$-sparse $x'$}}{\min} 
	\underbrace{\norm{x-x'}{}}_{\text{various norm}}$)

\paragraph{Some notes about $A$} 
\begin{itemize}
\item If $A \in \R^{m \times n}$ satisfies RIP, then recovery is possible. 
\item When each entry in $A$ is sampled from a Gaussian with mean 0 and 
variance 1, then $m=O(n \log \frac{n}{k})$ suffices. 
\end{itemize}

\paragraph{How good is this?}
\begin{itemize}
\item to store the positions of the entries: 
	$\log \binom{n}{k} \approx k \log \frac{n}{k}$
\item to store the values of the entries: $k$ words
\end{itemize}

Define ``sparsity ratio'' $R = \frac{n}{k}$.

Compressed sensing saves $\frac{R}{\log R}$ factor relative to naive sampling. 

Storage saves approximately $R$ factor.

Can't use $O(k)$ measurements \underline{\em in general}.

\underline{\em But} can for more structured signals, e.g. {\em block-sparse} signals:
\begin{figure}[H]
\centering
\includegraphics[width=.5\textwidth]{block-sparse-example.png}
\caption{$\frac{k}{B}$ ``blocks'' of length $B$ where each block is all on/all off}
\end{figure}

For block-sparse signals, the number of support is 
$\binom{\frac{n}{B}}{\frac{k}{B}} = 2^{O(\frac{k}{B} \log \frac{n}{k})}$. 
When $B \geq \log \frac{n}{k}$ this is $2^{O(k)}$. 

\section{Tree sparsity}
\begin{figure}[H]
\centering
\includegraphics[width=0.5\textwidth]{tree-sparsity-example.png}
\caption{Sparsity pattern is contiguous rooted subtree}
\end{figure}

\paragraph{Number of trees with $k$ terms in size $n$ binary tree}
\begin{itemize}
\item path that visits all vertices $\leq 2k$ edges
\item at each vertex there are 3 possible directions to go
\end{itemize}
number of trees $\leq 3^{2k} = 2^{O(k)}$

\newcommand{\F}{\mathcal{F}}
\section{Model sparsity}
$\F$ is a family of supports, each $S$ in $\F$ satisfies $S \subseteq [n]$, 
$|S| \leq k$. 

\begin{theorem}
$m = O(k + \log |\F|)$ Gaussian measurements suffice. 
\end{theorem}

\newcommand{\supp}{\text{supp}}

\paragraph{Model based compressed sensing}
Given $y=Ax+e$, $\supp(x) \in \F$, recover $\hat{x}$ such that 
$\norm{\hat{x} - x}{2} \lesssim \norm{e}{2}$

\paragraph{Model RIP}
$\forall x$ with $\supp(x) \in \F \oplus \F = 
	\left\{S \cup T | S, T \in \F \right\}$
\begin{equation}
\norm{Ax}{2} = (1 \pm \eps) \norm{x}{2}
\end{equation}

\paragraph{Model IHT}
\begin{equation}
x^{i+1} = H_{\F}(x^i+A^T(y-Ax^i))
\end{equation}
where
\begin{equation}
H_{\F} = \underset{T \in \F}{\arg} \min \norm{z_T}{2}
\end{equation}

(Before in {\em regular} compressed sensing if $A$ satisfies $2k$-RIP then 
IHT works.)

\paragraph{First iteration analysis}
\begin{equation}
z = A^T y=A^T Ax+A^T e
\end{equation}

$\forall T$ and $S=\supp(x)$, if $A$ is model RIP on $\F \oplus \F$, 
\begin{align}
\norm{z-x_{S \cup T}}{2} & \leq \norm{A^T A - I}{2} \norm{x}{2} + 
	\norm{A^T_{S \cup T \times [n]}}{2} \norm{e}{2} \\
& \leq \eps \norm{x}{2} + (1 + \eps) \norm{e}{2} 
\end{align}

$\forall z$ and $\underbrace{T}_{\text{top $k$ of $z$}} \in \F$ we want 
\begin{equation}
\label{mcs-bound1}
\norm{x-z_T}{2} \lesssim \norm{(x-z)_{S \cup T}}{2}
\end{equation}

To prove \eqref{mcs-bound1}:
\begin{align}
\norm{x_{S \backslash T}}{2} & \leq 
	\norm{(x-z)_{S \backslash T}}{2} + \norm{z_{S \backslash T}}{2} \\
& \leq \norm{(x-z)_{S \backslash T}}{2} + \norm{z_{T \backslash S}}{2} \\
\Rightarrow \norm{x_{S \backslash T}}{2}^2 &\leq 
	2\norm{(x-z)_{S \backslash T}}{2}^2 + 2\norm{z_{T \backslash S}}{2}^2
\end{align}

\begin{align}
\norm{x-z_T}{2}^2 &= \norm{x_{s \backslash T}}{2}^2 
	+ \norm{z_{T \backslash S}}{2}^2 + \norm{(x-z)_{T \cap S}}{2}^2 \\
& \leq 2 \norm{(x-z)_{S \backslash T}}{2}^2 + 3 \norm{z^{T \backslash S}}{2}^2 
	+ \norm{(x-z)^{T \cap S}}{2}^2 \\
& \leq 3 \norm{(x-z)_{S \cup T}}{2}^2
\end{align}

\paragraph{Running time}
\begin{itemize}
\item regular IHT: $\log \frac{\norm{x}{2}}{\norm{e}{2}}$ (matrix vector 
	multiplication time for $A$)
\item model IHT: $\log \frac{\norm{x}{2}}{\norm{e}{2}}$ (matrix vector 
	multiplication time for $A$ $+$ $H_{\F}$)
\end{itemize}

\paragraph{Computing $H_{\F}$ for trees}
\begin{itemize}
\item exact:  $O(nk^2)$, $O(nk)$
\item approximate (find $T'$ such that $\norm{z_{T'}}{2} \lesssim 
		\underset{T}{\min} \norm{z_T}{2}$): 
	$\tilde{O}(n)$
\end{itemize}

\section{Compressed sensing using $L^1$ minimization}
For 
\begin{equation}
y = Ax+e
\end{equation}

\begin{equation}
\min \norm{x}{1}
\end{equation}
given
\begin{equation}
\norm{A\hat{x}-y}{2} \leq \eps
\end{equation}

\begin{theorem}\label{cs-l1}
If $\eps \geq \norm{e}{2}$ and $A$ satisfies RIP or RE then 
$\norm{\hat{x} - x}{2} \lesssim \eps$.
\end{theorem}

\subsection{Restricted Eigenvalue (RE)}
\paragraph{IHT fails for $A=2I$}
\begin{align}
z &= A^T A x + A^T e \\
&= 4x + 2e
\end{align}

\begin{definition}{Restricted Eigenvalue (RE)}
\begin{equation}
\frac{\norm{Az}{2}}{\norm{z}{2}} \geq \eps
\end{equation}
whenever
\begin{align}
|S| &= k \\
\norm{z_S}{1} &\geq \alpha \norm{z_{\bar{S}}}{1}
\end{align}
\end{definition}
For example, $\eps = \frac{1}{10}$ and $\alpha =1 $.

\begin{proof}{(Theorem \ref{cs-l1})}
Set $\eps = \norm{e}{2}$.

Let $z = \hat{x} - x$. 

  \begin{align*}
\norm{Az-e}{2}^2 &\leq \norm{e}{2}^2\\
\norm{Az}{2}^2 - 2 e^TAz + \norm{e}{2}^2&\leq  \norm{e}{2}^2\\
\Rightarrow \norm{Az}{2} &\leq 2 \norm{e}{2} 
  \end{align*}

For $S = \supp(x)$, 
\begin{align*}
\norm{x_S}{1} = \norm{x}{1} & \geq \norm{\hat{x}}{1} \\
& = \norm{x+z}{1} \\
& \geq \norm{(x+z)_S}{1} + \norm{z_{\bar{S}}}{1} \\
& \geq \norm{x_S}{1} + \norm{z_{\bar{S}}}{1} - \norm{z_{S}}{1}
\end{align*}
so $\norm{z_{S}}{1} \geq \norm{z_{\bar{S}}}{1}$. 

RE $\Rightarrow$ $\norm{z}{2} \lesssim \norm{Az}{2} \leq 2 \norm{e}{2}$.
\end{proof}

\subsection{RIP $\Rightarrow$ RE}
\paragraph{``Shelling argument''}
Suppose $A$ satisfies the RIP of order $2k$.  We would like to show
for any $z$ and $S \subset [n]$ of size $k$ with $\norm{z_S}{1} \geq
\norm{z_{\overline{S}}}{1}$ that $\norm{Az}{2} \gtrsim \norm{z}{2}$.

Split $z$ into blocks $z^1, z^2, \dots$ of decreasing magnitude, so
$z^1$ has the largest $k$ coordinates, and each next $z^i$ has the
next largest $2k$ coordinates.  Then for $i \geq 3$ we have that
\begin{equation}
\frac{\norm{z^i}{2}}{\sqrt{2k}} \leq \norm{z^i}{2} \leq 
	\frac{\norm{z^{i-1}}{2}}{\sqrt{2k}}
\end{equation}
By assumption, $\norm{z^1}{1} \geq \norm{\sum_{i=2}^{\infty} z^i}{1}$.  Then

\begin{align*}
\norm{Az}{} & = \norm{A(z^1 + z^2 + \dots)}{} \\ 
& \geq \norm{A(z^1+z^2)}{} - \norm{Az^3}{} - \dots\\
& \geq (1-\eps)\norm{z^1 + z^2}{2} - (1+\eps)(\sum_{i=3}^\infty \norm{z^i}{2})\\
& \geq (1-\eps)\norm{z^1}{2} - (1+\eps)(\sum_{i=2}^\infty \norm{z^i}{1})/\sqrt{2k}\\
& = (1-\eps)\norm{z^1}{2} - \frac{(1+\eps)}{\sqrt{2k}}\norm{\sum_{i=2}^\infty z^i}{1}\\
& = (1-\eps)\norm{z^1}{2} - \frac{(1+\eps)}{\sqrt{2k}}\norm{\sum_{i=2}^\infty z^i}{1}\\
& \geq (1-\eps)\norm{z^1}{2} - \frac{(1+\eps)}{\sqrt{2k}}\norm{z^1}{1}\\
& \geq (1-\eps)\norm{z^1}{2} - \frac{(1+\eps)}{\sqrt{2}}\norm{z^1}{2}\\
& \geq \frac{1}{10} \norm{z^1}{2}
\end{align*}

for $\eps <  1/10$.


\bibliographystyle{alpha}

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\bibitem[CRT06]{CRT06}
Candes, E. J., Romberg, J. K., Tao, T.
\newblock Stable signal recovery from incomplete and inaccurate measurements.
\newblock {\em Communications on Pure and Applied Mathematics}, 59(8), 1207-1223.

\bibitem[BCDH10]{BCDH10}
R. Baraniuk, V. Cevher, M. F. Duarte, C. Hegde.
\newblock Model-based compressive sensing.
\newblock {\em IEEE Transactions on Information Theory}, 59(8), vol. 56, num. 4, p. 1982-2001, 2010.

\end{thebibliography}

\end{document}


