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java.lang.Objectjml.recovery.MatrixCompletion
public class MatrixCompletion
A Java implementation of matrix completion which solves the following convex optimization problem: min ||A||_* s.t. D = A + E E(Omega) = 0 where ||.||_* denotes the nuclear norm of a matrix (i.e., the sum of its singular values). Inexact augmented Lagrange multiplers is used to solve the optimization problem due to its empirically fast convergence speed and proved convergence to the true optimal solution. Input: D: an observation matrix with columns as data vectors Omega: a sparse or dense logical matrix indicating the indices of samples Output: A: a low-rank matrix completed from the data matrix D E: error matrix between D and A
Field Summary | |
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(package private) org.apache.commons.math.linear.RealMatrix |
A
A low-rank matrix recovered from the corrupted data observation matrix D. |
(package private) org.apache.commons.math.linear.RealMatrix |
D
Observation real matrix. |
(package private) org.apache.commons.math.linear.RealMatrix |
E
Error matrix between the original observation matrix D and the low-rank recovered matrix A. |
(package private) org.apache.commons.math.linear.RealMatrix |
Omega
a sparse or dense logical matrix indicating the indices of samples |
Constructor Summary | |
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MatrixCompletion()
Constructor. |
Method Summary | |
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void |
feedData(org.apache.commons.math.linear.RealMatrix D)
Feed an observation matrix. |
void |
feedIndices(int[] indices)
Feed indices of samples. |
void |
feedIndices(org.apache.commons.math.linear.RealMatrix Omega)
Feed indices of samples. |
org.apache.commons.math.linear.RealMatrix |
GetErrorMatrix()
Get the error matrix between the original observation matrix and its low-rank completed matrix. |
org.apache.commons.math.linear.RealMatrix |
GetLowRankEstimation()
Get the low-rank completed matrix. |
static void |
main(java.lang.String[] args)
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org.apache.commons.math.linear.RealMatrix[] |
matrixCompletion(org.apache.commons.math.linear.RealMatrix D,
int[] indices)
Do matrix completion which solves the following convex optimization problem: min ||A||_* s.t. |
org.apache.commons.math.linear.RealMatrix[] |
matrixCompletion(org.apache.commons.math.linear.RealMatrix D,
org.apache.commons.math.linear.RealMatrix Omega)
Do matrix completion which solves the following convex optimization problem: min ||A||_* s.t. |
void |
run()
Run matrix completion. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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org.apache.commons.math.linear.RealMatrix D
org.apache.commons.math.linear.RealMatrix Omega
org.apache.commons.math.linear.RealMatrix A
org.apache.commons.math.linear.RealMatrix E
Constructor Detail |
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public MatrixCompletion()
Method Detail |
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public static void main(java.lang.String[] args)
args
- public void feedData(org.apache.commons.math.linear.RealMatrix D)
D
- a real matrixpublic void feedIndices(org.apache.commons.math.linear.RealMatrix Omega)
Omega
- a sparse or dense logical matrix indicating the indices of samplespublic void feedIndices(int[] indices)
indices
- an int
array for the indices of samplespublic void run()
public org.apache.commons.math.linear.RealMatrix GetLowRankEstimation()
public org.apache.commons.math.linear.RealMatrix GetErrorMatrix()
public org.apache.commons.math.linear.RealMatrix[] matrixCompletion(org.apache.commons.math.linear.RealMatrix D, org.apache.commons.math.linear.RealMatrix Omega)
D
- a real observation matrixOmega
- a sparse or dense logical matrix indicating the indices of samples
RealMatrix
array [A, E] where A is the low-rank
completion from D, and E is the error matrix between A and Dpublic org.apache.commons.math.linear.RealMatrix[] matrixCompletion(org.apache.commons.math.linear.RealMatrix D, int[] indices)
D
- a real observation matrixindices
- an int
array for the indices of samples
RealMatrix
array [A, E] where A is the low-rank
completion from D, and E is the error matrix between A and D
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