Description Usage Arguments Details Value Note Author(s) References Examples
Performs discriminative component analysis on the given data.
1 
data 

chunks 
length 
neglinks 

useD 
Integer. Optional. When not given, DCA is done in the original dimension and B is full rank. When useD is given, DCA is preceded by constraints based LDA which reduces the dimension to useD. B in this case is of rank useD. 
Put DCA function details here.
list of the DCA results:
B 
DCA suggested Mahalanobis matrix 
DCA 
DCA suggested transformation of the data. The dimension is (original data dimension) * (useD) 
newData 
DCA transformed data 
For every two original data points (x1, x2) in newData (y1, y2):
(x2  x1)' * B * (x2  x1) =  (x2  x1) * A ^2 =  y2  y1 ^2
Put some note here.
Xiao Nan <http://www.road2stat.com>
Steven C.H. Hoi, W. Liu, M.R. Lyu and W.Y. Ma (2006). Learning Distance Metrics with Contextual Constraints for Image Retrieval. Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR2006).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  ## Not run:
set.seed(123)
require(MASS) # generate synthetic Gaussian data
k = 100 # sample size of each class
n = 3 # specify how many class
N = k * n # total sample number
x1 = mvrnorm(k, mu = c(10, 6), matrix(c(10, 4, 4, 10), ncol = 2))
x2 = mvrnorm(k, mu = c(0, 0), matrix(c(10, 4, 4, 10), ncol = 2))
x3 = mvrnorm(k, mu = c(10, 6), matrix(c(10, 4, 4, 10), ncol = 2))
data = as.data.frame(rbind(x1, x2, x3))
# The fully labeled data set with 3 classes
plot(data$V1, data$V2, bg = c("#E41A1C", "#377EB8", "#4DAF4A")[gl(n, k)],
pch = c(rep(22, k), rep(21, k), rep(25, k)))
Sys.sleep(3)
# Same data unlabeled; clearly the classes' structure is less evident
plot(x$V1, x$V2)
Sys.sleep(3)
chunk1 = sample(1:100, 5)
chunk2 = sample(setdiff(1:100, chunk1), 5)
chunk3 = sample(101:200, 5)
chunk4 = sample(setdiff(101:200, chunk3), 5)
chunk5 = sample(201:300, 5)
chks = list(chunk1, chunk2, chunk3, chunk4, chunk5)
chunks = rep(1, 300)
# positive samples in the chunks
for (i in 1:5) {
for (j in chks[[i]]) {
chunks[j] = i
}
}
# define the negative constrains between chunks
neglinks = matrix(c(
0, 0, 1, 1, 1,
0, 0, 1, 1, 1,
1, 1, 0, 0, 0,
1, 1, 0, 0, 1,
1, 1, 1, 1, 0),
ncol = 5, byrow = TRUE)
dcaData = dca(data = data, chunks = chunks, neglinks = neglinks)$newData
# plot DCA transformed data
plot(dcaData[, 1], dcaData[, 2], bg = c("#E41A1C", "#377EB8", "#4DAF4A")[gl(n, k)],
pch = c(rep(22, k), rep(21, k), rep(25, k)),
xlim = c(15, 15), ylim = c(15, 15))
## End(Not run)

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