ssvd-package: Sparse SVD
In ssvd: Sparse SVD
Description
Details
Author(s)
References
Examples
Description
Obtain sparse SVD using fast iterative thresholding method, together with
a fast initialization algorithm
Details
Package: ssvd
Type: Package
Version: 1.0
Date: 2013-09-25
License: GPL (>= 2)
There are three main functions of the package: ssvd, ssvd.initial,
and ssvd.iter.thresh
Author(s)
Dan Yang <dyang@stat.rutgers.edu>
References
A Sparse SVD Method for High-dimensional Data
Examples
1
2
3
4
ssvd(matrix(rnorm(2^15),2^7,2^8), method = "method")
ans.initial <- ssvd.initial(matrix(rnorm(2^15),2^7,2^8), method = "method")
ans.iter <- ssvd.iter.thresh(matrix(rnorm(2^15),2^7,2^8),
u.old=ans.initial$u, v.old= ans.initial$v, method = "method")
Example output
$u
[,1]
[1,] 0.0000000
[2,] 0.7836273
[3,] 0.0000000
[4,] 0.0000000
[5,] 0.0000000
[6,] 0.0000000
[7,] 0.0000000
[8,] 0.0000000
[9,] 0.0000000
[10,] 0.0000000
[11,] 0.0000000
[12,] 0.0000000
[13,] 0.0000000
[14,] 0.0000000
[15,] 0.0000000
[16,] 0.0000000
[17,] 0.0000000
[18,] 0.0000000
[19,] 0.0000000
[20,] 0.0000000
[21,] 0.0000000
[22,] 0.0000000
[23,] 0.0000000
[24,] 0.0000000
[25,] 0.0000000
[26,] 0.0000000
[27,] 0.0000000
[28,] 0.0000000
[29,] 0.0000000
[30,] 0.0000000
[31,] 0.0000000
[32,] 0.0000000
[33,] 0.0000000
[34,] 0.0000000
[35,] 0.0000000
[36,] 0.0000000
[37,] 0.0000000
[38,] 0.0000000
[39,] 0.0000000
[40,] 0.0000000
[41,] 0.0000000
[42,] 0.0000000
[43,] 0.0000000
[44,] 0.0000000
[45,] 0.0000000
[46,] 0.0000000
[47,] 0.0000000
[48,] 0.0000000
[49,] 0.0000000
[50,] 0.0000000
[51,] 0.0000000
[52,] 0.0000000
[53,] 0.0000000
[54,] 0.0000000
[55,] 0.0000000
[56,] 0.0000000
[57,] 0.0000000
[58,] 0.0000000
[59,] 0.0000000
[60,] 0.0000000
[61,] 0.0000000
[62,] 0.0000000
[63,] 0.0000000
[64,] 0.0000000
[65,] 0.0000000
[66,] 0.0000000
[67,] 0.0000000
[68,] 0.0000000
[69,] 0.0000000
[70,] 0.0000000
[71,] 0.0000000
[72,] 0.0000000
[73,] 0.0000000
[74,] 0.0000000
[75,] 0.0000000
[76,] 0.0000000
[77,] 0.0000000
[78,] 0.0000000
[79,] 0.0000000
[80,] 0.0000000
[81,] 0.0000000
[82,] 0.0000000
[83,] 0.0000000
[84,] 0.0000000
[85,] 0.0000000
[86,] 0.0000000
[87,] 0.0000000
[88,] 0.0000000
[89,] 0.0000000
[90,] 0.0000000
[91,] 0.0000000
[92,] 0.0000000
[93,] 0.0000000
[94,] 0.0000000
[95,] 0.0000000
[96,] 0.0000000
[97,] 0.0000000
[98,] 0.0000000
[99,] 0.0000000
[100,] 0.0000000
[101,] 0.0000000
[102,] 0.0000000
[103,] 0.0000000
[104,] 0.0000000
[105,] 0.0000000
[106,] 0.0000000
[107,] 0.0000000
[108,] 0.0000000
[109,] 0.0000000
[110,] 0.0000000
[111,] 0.0000000
[112,] 0.0000000
[113,] 0.0000000
[114,] 0.0000000
[115,] 0.0000000
[116,] 0.0000000
[117,] 0.0000000
[118,] 0.0000000
[119,] 0.0000000
[120,] 0.0000000
[121,] 0.0000000
[122,] 0.0000000
[123,] 0.0000000
[124,] 0.0000000
[125,] 0.6212312
[126,] 0.0000000
[127,] 0.0000000
[128,] 0.0000000
$v
[,1]
[1,] 0
[2,] 0
[3,] 0
[4,] 0
[5,] 0
[6,] 0
[7,] 0
[8,] 0
[9,] 0
[10,] 0
[11,] 0
[12,] 0
[13,] 0
[14,] 0
[15,] 0
[16,] 0
[17,] 0
[18,] 0
[19,] 0
[20,] 0
[21,] 0
[22,] 0
[23,] 0
[24,] 0
[25,] 0
[26,] 0
[27,] 0
[28,] 0
[29,] 0
[30,] 0
[31,] 0
[32,] 0
[33,] 0
[34,] 0
[35,] 0
[36,] 0
[37,] 0
[38,] 0
[39,] 0
[40,] 0
[41,] 0
[42,] 0
[43,] 0
[44,] 0
[45,] 0
[46,] 0
[47,] 0
[48,] 0
[49,] 0
[50,] 0
[51,] 0
[52,] 0
[53,] 0
[54,] 0
[55,] 0
[56,] 0
[57,] 0
[58,] 0
[59,] 0
[60,] 0
[61,] 0
[62,] 0
[63,] 0
[64,] 0
[65,] 0
[66,] 0
[67,] 0
[68,] 0
[69,] 0
[70,] 0
[71,] 0
[72,] 0
[73,] 0
[74,] 0
[75,] 0
[76,] 0
[77,] 0
[78,] 0
[79,] 0
[80,] 0
[81,] 0
[82,] 0
[83,] 0
[84,] 0
[85,] 0
[86,] 0
[87,] 0
[88,] 0
[89,] 0
[90,] 0
[91,] 0
[92,] 0
[93,] 0
[94,] 0
[95,] 0
[96,] 0
[97,] 0
[98,] 0
[99,] 0
[100,] 0
[101,] 0
[102,] 0
[103,] 0
[104,] 0
[105,] 0
[106,] 0
[107,] 0
[108,] 0
[109,] 0
[110,] 0
[111,] 0
[112,] 0
[113,] 0
[114,] 0
[115,] 0
[116,] 0
[117,] 0
[118,] 0
[119,] 0
[120,] 0
[121,] 0
[122,] 0
[123,] 0
[124,] 0
[125,] 0
[126,] 0
[127,] 0
[128,] 0
[129,] 0
[130,] 0
[131,] 0
[132,] 0
[133,] 0
[134,] 0
[135,] 0
[136,] 0
[137,] 0
[138,] 0
[139,] 0
[140,] 0
[141,] 0
[142,] 0
[143,] 0
[144,] 0
[145,] 0
[146,] 0
[147,] 0
[148,] 0
[149,] 0
[150,] 0
[151,] 0
[152,] 0
[153,] 0
[154,] 0
[155,] 0
[156,] 0
[157,] 0
[158,] 0
[159,] 0
[160,] 0
[161,] 0
[162,] 0
[163,] 0
[164,] 0
[165,] 0
[166,] 0
[167,] 0
[168,] 0
[169,] 0
[170,] 0
[171,] 0
[172,] 0
[173,] 0
[174,] 0
[175,] 1
[176,] 0
[177,] 0
[178,] 0
[179,] 0
[180,] 0
[181,] 0
[182,] 0
[183,] 0
[184,] 0
[185,] 0
[186,] 0
[187,] 0
[188,] 0
[189,] 0
[190,] 0
[191,] 0
[192,] 0
[193,] 0
[194,] 0
[195,] 0
[196,] 0
[197,] 0
[198,] 0
[199,] 0
[200,] 0
[201,] 0
[202,] 0
[203,] 0
[204,] 0
[205,] 0
[206,] 0
[207,] 0
[208,] 0
[209,] 0
[210,] 0
[211,] 0
[212,] 0
[213,] 0
[214,] 0
[215,] 0
[216,] 0
[217,] 0
[218,] 0
[219,] 0
[220,] 0
[221,] 0
[222,] 0
[223,] 0
[224,] 0
[225,] 0
[226,] 0
[227,] 0
[228,] 0
[229,] 0
[230,] 0
[231,] 0
[232,] 0
[233,] 0
[234,] 0
[235,] 0
[236,] 0
[237,] 0
[238,] 0
[239,] 0
[240,] 0
[241,] 0
[242,] 0
[243,] 0
[244,] 0
[245,] 0
[246,] 0
[247,] 0
[248,] 0
[249,] 0
[250,] 0
[251,] 0
[252,] 0
[253,] 0
[254,] 0
[255,] 0
[256,] 0
$d
[1] 3.379913
$niter
[1] 2
$sigma.hat
[1] 0.9976234
$dist.u
[1] 0
$dist.v
[1] 0
Warning messages:
1: In ssvd.initial(x, method = method, alpha.method = alpha.method, :
SSVD.initial: Number of selected rows less than rank!
2: In ssvd.initial(x, method = method, alpha.method = alpha.method, :
SSVD.initial: Number of selected cols less than rank!
3: In ssvd.iter.thresh(x, method = method, u.old = ans.initial$u, v.old = ans.initial$v, :
SSVD.iter.thresh: Overthresh: all zero!
Warning messages:
1: In ssvd.initial(matrix(rnorm(2^15), 2^7, 2^8), method = "method") :
SSVD.initial: Number of selected rows less than rank!
2: In ssvd.initial(matrix(rnorm(2^15), 2^7, 2^8), method = "method") :
SSVD.initial: Number of selected cols less than rank!
Warning message:
In ssvd.iter.thresh(matrix(rnorm(2^15), 2^7, 2^8), u.old = ans.initial$u, :
SSVD.iter.thresh: Overthresh: all zero!
ssvd documentation built on May 1, 2019, 10:32 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References Examples
Description
Obtain sparse SVD using fast iterative thresholding method, together with a fast initialization algorithm
Details
| Package: | ssvd |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2013-09-25 |
| License: | GPL (>= 2) |
There are three main functions of the package: ssvd, ssvd.initial, and ssvd.iter.thresh
Author(s)
Dan Yang <dyang@stat.rutgers.edu>
References
A Sparse SVD Method for High-dimensional Data
Examples
1 2 3 4 | ssvd(matrix(rnorm(2^15),2^7,2^8), method = "method")
ans.initial <- ssvd.initial(matrix(rnorm(2^15),2^7,2^8), method = "method")
ans.iter <- ssvd.iter.thresh(matrix(rnorm(2^15),2^7,2^8),
u.old=ans.initial$u, v.old= ans.initial$v, method = "method")
|
Example output
$u
[,1]
[1,] 0.0000000
[2,] 0.7836273
[3,] 0.0000000
[4,] 0.0000000
[5,] 0.0000000
[6,] 0.0000000
[7,] 0.0000000
[8,] 0.0000000
[9,] 0.0000000
[10,] 0.0000000
[11,] 0.0000000
[12,] 0.0000000
[13,] 0.0000000
[14,] 0.0000000
[15,] 0.0000000
[16,] 0.0000000
[17,] 0.0000000
[18,] 0.0000000
[19,] 0.0000000
[20,] 0.0000000
[21,] 0.0000000
[22,] 0.0000000
[23,] 0.0000000
[24,] 0.0000000
[25,] 0.0000000
[26,] 0.0000000
[27,] 0.0000000
[28,] 0.0000000
[29,] 0.0000000
[30,] 0.0000000
[31,] 0.0000000
[32,] 0.0000000
[33,] 0.0000000
[34,] 0.0000000
[35,] 0.0000000
[36,] 0.0000000
[37,] 0.0000000
[38,] 0.0000000
[39,] 0.0000000
[40,] 0.0000000
[41,] 0.0000000
[42,] 0.0000000
[43,] 0.0000000
[44,] 0.0000000
[45,] 0.0000000
[46,] 0.0000000
[47,] 0.0000000
[48,] 0.0000000
[49,] 0.0000000
[50,] 0.0000000
[51,] 0.0000000
[52,] 0.0000000
[53,] 0.0000000
[54,] 0.0000000
[55,] 0.0000000
[56,] 0.0000000
[57,] 0.0000000
[58,] 0.0000000
[59,] 0.0000000
[60,] 0.0000000
[61,] 0.0000000
[62,] 0.0000000
[63,] 0.0000000
[64,] 0.0000000
[65,] 0.0000000
[66,] 0.0000000
[67,] 0.0000000
[68,] 0.0000000
[69,] 0.0000000
[70,] 0.0000000
[71,] 0.0000000
[72,] 0.0000000
[73,] 0.0000000
[74,] 0.0000000
[75,] 0.0000000
[76,] 0.0000000
[77,] 0.0000000
[78,] 0.0000000
[79,] 0.0000000
[80,] 0.0000000
[81,] 0.0000000
[82,] 0.0000000
[83,] 0.0000000
[84,] 0.0000000
[85,] 0.0000000
[86,] 0.0000000
[87,] 0.0000000
[88,] 0.0000000
[89,] 0.0000000
[90,] 0.0000000
[91,] 0.0000000
[92,] 0.0000000
[93,] 0.0000000
[94,] 0.0000000
[95,] 0.0000000
[96,] 0.0000000
[97,] 0.0000000
[98,] 0.0000000
[99,] 0.0000000
[100,] 0.0000000
[101,] 0.0000000
[102,] 0.0000000
[103,] 0.0000000
[104,] 0.0000000
[105,] 0.0000000
[106,] 0.0000000
[107,] 0.0000000
[108,] 0.0000000
[109,] 0.0000000
[110,] 0.0000000
[111,] 0.0000000
[112,] 0.0000000
[113,] 0.0000000
[114,] 0.0000000
[115,] 0.0000000
[116,] 0.0000000
[117,] 0.0000000
[118,] 0.0000000
[119,] 0.0000000
[120,] 0.0000000
[121,] 0.0000000
[122,] 0.0000000
[123,] 0.0000000
[124,] 0.0000000
[125,] 0.6212312
[126,] 0.0000000
[127,] 0.0000000
[128,] 0.0000000
$v
[,1]
[1,] 0
[2,] 0
[3,] 0
[4,] 0
[5,] 0
[6,] 0
[7,] 0
[8,] 0
[9,] 0
[10,] 0
[11,] 0
[12,] 0
[13,] 0
[14,] 0
[15,] 0
[16,] 0
[17,] 0
[18,] 0
[19,] 0
[20,] 0
[21,] 0
[22,] 0
[23,] 0
[24,] 0
[25,] 0
[26,] 0
[27,] 0
[28,] 0
[29,] 0
[30,] 0
[31,] 0
[32,] 0
[33,] 0
[34,] 0
[35,] 0
[36,] 0
[37,] 0
[38,] 0
[39,] 0
[40,] 0
[41,] 0
[42,] 0
[43,] 0
[44,] 0
[45,] 0
[46,] 0
[47,] 0
[48,] 0
[49,] 0
[50,] 0
[51,] 0
[52,] 0
[53,] 0
[54,] 0
[55,] 0
[56,] 0
[57,] 0
[58,] 0
[59,] 0
[60,] 0
[61,] 0
[62,] 0
[63,] 0
[64,] 0
[65,] 0
[66,] 0
[67,] 0
[68,] 0
[69,] 0
[70,] 0
[71,] 0
[72,] 0
[73,] 0
[74,] 0
[75,] 0
[76,] 0
[77,] 0
[78,] 0
[79,] 0
[80,] 0
[81,] 0
[82,] 0
[83,] 0
[84,] 0
[85,] 0
[86,] 0
[87,] 0
[88,] 0
[89,] 0
[90,] 0
[91,] 0
[92,] 0
[93,] 0
[94,] 0
[95,] 0
[96,] 0
[97,] 0
[98,] 0
[99,] 0
[100,] 0
[101,] 0
[102,] 0
[103,] 0
[104,] 0
[105,] 0
[106,] 0
[107,] 0
[108,] 0
[109,] 0
[110,] 0
[111,] 0
[112,] 0
[113,] 0
[114,] 0
[115,] 0
[116,] 0
[117,] 0
[118,] 0
[119,] 0
[120,] 0
[121,] 0
[122,] 0
[123,] 0
[124,] 0
[125,] 0
[126,] 0
[127,] 0
[128,] 0
[129,] 0
[130,] 0
[131,] 0
[132,] 0
[133,] 0
[134,] 0
[135,] 0
[136,] 0
[137,] 0
[138,] 0
[139,] 0
[140,] 0
[141,] 0
[142,] 0
[143,] 0
[144,] 0
[145,] 0
[146,] 0
[147,] 0
[148,] 0
[149,] 0
[150,] 0
[151,] 0
[152,] 0
[153,] 0
[154,] 0
[155,] 0
[156,] 0
[157,] 0
[158,] 0
[159,] 0
[160,] 0
[161,] 0
[162,] 0
[163,] 0
[164,] 0
[165,] 0
[166,] 0
[167,] 0
[168,] 0
[169,] 0
[170,] 0
[171,] 0
[172,] 0
[173,] 0
[174,] 0
[175,] 1
[176,] 0
[177,] 0
[178,] 0
[179,] 0
[180,] 0
[181,] 0
[182,] 0
[183,] 0
[184,] 0
[185,] 0
[186,] 0
[187,] 0
[188,] 0
[189,] 0
[190,] 0
[191,] 0
[192,] 0
[193,] 0
[194,] 0
[195,] 0
[196,] 0
[197,] 0
[198,] 0
[199,] 0
[200,] 0
[201,] 0
[202,] 0
[203,] 0
[204,] 0
[205,] 0
[206,] 0
[207,] 0
[208,] 0
[209,] 0
[210,] 0
[211,] 0
[212,] 0
[213,] 0
[214,] 0
[215,] 0
[216,] 0
[217,] 0
[218,] 0
[219,] 0
[220,] 0
[221,] 0
[222,] 0
[223,] 0
[224,] 0
[225,] 0
[226,] 0
[227,] 0
[228,] 0
[229,] 0
[230,] 0
[231,] 0
[232,] 0
[233,] 0
[234,] 0
[235,] 0
[236,] 0
[237,] 0
[238,] 0
[239,] 0
[240,] 0
[241,] 0
[242,] 0
[243,] 0
[244,] 0
[245,] 0
[246,] 0
[247,] 0
[248,] 0
[249,] 0
[250,] 0
[251,] 0
[252,] 0
[253,] 0
[254,] 0
[255,] 0
[256,] 0
$d
[1] 3.379913
$niter
[1] 2
$sigma.hat
[1] 0.9976234
$dist.u
[1] 0
$dist.v
[1] 0
Warning messages:
1: In ssvd.initial(x, method = method, alpha.method = alpha.method, :
SSVD.initial: Number of selected rows less than rank!
2: In ssvd.initial(x, method = method, alpha.method = alpha.method, :
SSVD.initial: Number of selected cols less than rank!
3: In ssvd.iter.thresh(x, method = method, u.old = ans.initial$u, v.old = ans.initial$v, :
SSVD.iter.thresh: Overthresh: all zero!
Warning messages:
1: In ssvd.initial(matrix(rnorm(2^15), 2^7, 2^8), method = "method") :
SSVD.initial: Number of selected rows less than rank!
2: In ssvd.initial(matrix(rnorm(2^15), 2^7, 2^8), method = "method") :
SSVD.initial: Number of selected cols less than rank!
Warning message:
In ssvd.iter.thresh(matrix(rnorm(2^15), 2^7, 2^8), u.old = ans.initial$u, :
SSVD.iter.thresh: Overthresh: all zero!
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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