.\"Text automatically generated by txt2man
.TH local_coordinate_coding  "1" "" ""
.SH NAME
\fBlocal_coordinate_coding \fP- local coordinate coding
.SH SYNOPSIS
.nf
.fam C
 \fBlocal_coordinate_coding\fP [\fB-h\fP] [\fB-v\fP] \fB-k\fP \fIint\fP \fB-i\fP \fIstring\fP [\fB-c\fP \fIstring\fP] [\fB-d\fP \fIstring\fP] [\fB-D\fP \fIstring\fP] [\fB-l\fP \fIdouble\fP] [\fB-n\fP \fIint\fP] [\fB-N\fP] [\fB-o\fP \fIdouble\fP] [\fB-s\fP \fIint\fP] \fB-V\fP 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


An implementation of Local Coordinate Coding (LCC), which codes data that
approximately lives on a manifold using a variation of l1-norm regularized
sparse coding. Given a dense data matrix X with n points and d dimensions,
LCC seeks to find a dense dictionary matrix D with k atoms in d dimensions,
and a coding matrix Z with n points in k dimensions. Because of the
regularization method used, the atoms in D should lie close to the manifold on
which the data points lie.
.PP
The original data matrix X can then be reconstructed as D * Z. Therefore,
this program finds a representation of each point in X as a sparse linear
combination of atoms in the dictionary D.
.PP
The coding is found with an algorithm which alternates between a dictionary
step, which updates the dictionary D, and a coding step, which updates the
coding matrix Z.
.PP
To run this program, the input matrix X must be specified (with \fB-i\fP), along
with the number of atoms in the dictionary (\fB-k\fP). An initial dictionary may
also be specified with the \fB--initial_dictionary\fP option. The l1-norm
regularization parameter is specified with \fB-l\fP. For example, to run LCC on the
dataset in data.csv using 200 atoms and an l1-regularization parameter of 0.1,
saving the dictionary into dict.csv and the codes into codes.csv, use 
.PP
$ \fBlocal_coordinate_coding\fP \fB-i\fP data.csv \fB-k\fP 200 \fB-l\fP 0.1 \fB-d\fP dict.csv \fB-c\fP codes.csv
.PP
The maximum number of iterations may be specified with the \fB-n\fP option.
Optionally, the input data matrix X can be normalized before coding with the
\fB-N\fP option.
.SH REQUIRED OPTIONS 

.TP
.B
\fB--atoms\fP (\fB-k\fP) [\fIint\fP]
Number of atoms in the dictionary. 
.TP
.B
\fB--input_file\fP (\fB-i\fP) [\fIstring\fP]
Filename of the input data.  
.SH OPTIONS 

.TP
.B
\fB--codes_file\fP (\fB-c\fP) [\fIstring\fP]
Filename to save the output codes to. Default value 'codes.csv'. 
.TP
.B
\fB--dictionary_file\fP (\fB-d\fP) [\fIstring\fP]
Filename to save the output dictionary to.  Default value 'dictionary.csv'. 
.TP
.B
\fB--help\fP (\fB-h\fP)
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Get help on a specific module or option.  Default value ''. 
.TP
.B
\fB--initial_dictionary\fP (\fB-D\fP) [\fIstring\fP]
Filename for optional initial dictionary.  Default value ''. 
.TP
.B
\fB--lambda\fP (\fB-l\fP) [\fIdouble\fP]
Weighted l1-norm regularization parameter.  Default value 0. 
.TP
.B
\fB--max_iterations\fP (\fB-n\fP) [\fIint\fP]
Maximum number of iterations for LCC (0 indicates no limit). Default value 0. 
.TP
.B
\fB--normalize\fP (\fB-N\fP)
If set, the input data matrix will be normalized before coding. 
.TP
.B
\fB--objective_tolerance\fP (\fB-o\fP) [\fIdouble\fP]
Tolerance for objective function. Default value 0.01. 
.TP
.B
\fB--seed\fP (\fB-s\fP) [\fIint\fP]
Random seed. If 0, 'std::time(NULL)' is used.  Default value 0. 
.TP
.B
\fB--verbose\fP (\fB-v\fP)
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP)
Display the version of mlpack.
.SH ADDITIONAL INFORMATION

For further information, including relevant papers, citations, and theory,
consult the documentation found at http://www.mlpack.org or included with your
distribution of MLPACK.