# Difference between revisions of "large-Scale Supervised Sparse Principal Component Analysis"

(→1. Introduction) |
(→2. Primal problem) |
||

Line 21: | Line 21: | ||

Replacing the <math>\sqrt{\| Z \|_0}</math> with <math>\| Z \|_1</math> and dropping the rank constraint gives a relaxation of the original non-convex problem: | Replacing the <math>\sqrt{\| Z \|_0}</math> with <math>\| Z \|_1</math> and dropping the rank constraint gives a relaxation of the original non-convex problem: | ||

− | <math>max_z | + | <math>max_z Tr (\Sigma Z) - \lambda \| Z \|_1 : Z \succeq 0</math>, <math>Tr(Z)=1</math>. |

Fortunately, this relaxation approximates the original non-convex problem to a convex problem. | Fortunately, this relaxation approximates the original non-convex problem to a convex problem. | ||

+ | |||

+ | Here is an important theorem used by this paper: | ||

+ | |||

+ | Theorem(2.1) Let <math>\Sigma=A^T A</math> where <math>A=(a_1,a_2,......,a_n) \in {\mathbb R}^{m \times n}</math>, we have <math>\psi = max_{\| \xi \|_2=1}</math> <math>\sum_{i=1}^{n} (({a_i}^T \xi)^2 - \lambda)_+</math>. An optimal non-zero pattern corresponds to the indices <math>i</math> with <math>\lambda < (({a_i}^T \xi)^2-\lambda)_+</math> |

## Revision as of 05:35, 5 August 2013

## 1. Introduction

The drawbacks of most existing technique:

**1 Drawbacks of Existing techniques**

Existing techniques include ad-hoc methods(e.g. factor rotation techniques, simple thresholding), greedy algorithms, SCoTLASS, the regularized SVD method, SPCA, the generalized power method. These methods are based on non-convex optimization and they don't guarantee global optimum.

A semi-definite relaxation method called DSPCA can guarantee global convergence and has better performance than above algorithms, however, it is computationally expensive.

**2 Contribution of this paper**

This paper solves DSPCA in a computationally easier way, and hence it is a good solution for large scale data sets. This paper applies a block coordinate ascent algorithm with computational complexity [math]O(\hat{n^3})[/math], where [math]\hat{n}[/math] is the intrinsic dimension of the data. Since [math]\hat{n}[/math] could be very small compared to the dimension [math]n[/math] of the data, this algorithm is computationally easy.

## 2. Primal problem

The sparse PCA problem can be formulated as [math]max_x \ x^T \Sigma x - \lambda \| x \|_0 : \| x \|_2=1[/math].

This is equivalent to [math]max_z[/math] **Tr**[math]\Sigma Z - \lambda \sqrt{\| Z \|_0} : Z \succeq 0[/math], Tr [math]Z=1[/math], Rank[math](Z)=1[/math].

Replacing the [math]\sqrt{\| Z \|_0}[/math] with [math]\| Z \|_1[/math] and dropping the rank constraint gives a relaxation of the original non-convex problem:

[math]max_z Tr (\Sigma Z) - \lambda \| Z \|_1 : Z \succeq 0[/math], [math]Tr(Z)=1[/math].

Fortunately, this relaxation approximates the original non-convex problem to a convex problem.

Here is an important theorem used by this paper:

Theorem(2.1) Let [math]\Sigma=A^T A[/math] where [math]A=(a_1,a_2,......,a_n) \in {\mathbb R}^{m \times n}[/math], we have [math]\psi = max_{\| \xi \|_2=1}[/math] [math]\sum_{i=1}^{n} (({a_i}^T \xi)^2 - \lambda)_+[/math]. An optimal non-zero pattern corresponds to the indices [math]i[/math] with [math]\lambda \lt (({a_i}^T \xi)^2-\lambda)_+[/math]