elong0527
commented
5 years ago It is possible to have one simulated data that cluster most data into one cluster by chance, especially when signal to noise ratio is small.
Could you try to use the first 20 informative feature to run the S-AMA algorithm and see if you are able to get the 2 cluster correctly.
If yes try to add another 5 non-informative features and run the code. (Try to understand if the signal-to-noise ratio is too small in your current example)
In gerenal, the selection of kernel also have impact for the performance.
That will be helpful if you could also post the analysis results here (generate the results in rmarkdown and output in git_document format)
Cheers
` library(cvxclustr) library(scvxclustr) library(gglasso)
Generate a simple data matrix used for clustering, of which the rows are samples and the ###columns are variables. Here we produce a matrix consisted of 80 observations and 100 ###variables(the first 20 are informative variables). There exist 2 classes in this data.
class1 <- sample(1:80, 40) class2 <- setdiff(1:80, train) r1 <- rep(0, 80) r1[class1] <- 1
sample1_matrix <- matrix(0, nrow = 80, ncol = 20) for (i in which(r1 == 0)) { sample1_matrix[i, ] <- rnorm(20, mean = 1, sd = 0.5) }
for (i in which(r1 == 1)) { sample1_matrix[i, ] <- rnorm(20, mean = -1, sd = 0.5)
}
sample2_matrix <- matrix(rnorm(14400, mean = 4, sd = 1), nrow = 80, ncol = 80) sample_matrix <- cbind(sample1_matrix, sample2_matrix) sample_scale <- scale(sample_matrix, scale = F)
n <- nrow(sample_matrix) p <- ncol(sample_matrix)
construct sparse weights at first and then use it to take convex clustering by AMA ###algorithm from package "cvxclustr"
weight <- kernel_weights(t(sample_matrix), 0.5) weight <- knn_weights(weight, 5, n) weight <- weight/(sqrt(p) * sum(weight))
cvx_result <- cvxclust(X = t(sample_matrix), w = weight, gamma = 1e14, method = "ama", nu = 0.01)
construct adjacency matrix to get final clustering results. Here we can see the clustering ###results by object "cluster", and we find that most observations are classified into 2 classes.
A <- create_adjacency(cvx_result$V[[1]], weight, n, method = "ama") cluster <- find_clusters(A) cluster
Construct sparse weights at first and then use it to take convex clustering by AMA ###algorithm from package "cvxclustr". In this step we get the sparse weights for gamma_1 ###and the weights for gamma_2
weight <- kernel_weights(t(sample_scale), 0.5) weight <- knn_weights(weight, 5, n) weight <- weight/(sqrt(p) * sum(weight))
Gamma1 <- 1e15 Gamma2 <- 10
cvxresult <- cvxclust(t(sample_scale), nu = 0.01, weight, gamma = Gamma1, method = "ama", accelerate = F)$U[[1]] cvxresult <- t(cvxresult)
mu <- vector() for (j in 1:p) { mu[j] <- 1/(sqrt(sum(cvxresult[, j]^2)) + 1e-6) }
Gamma2_weight <- mu/(sqrt(n)*sum(mu))
nu <- AMA_step_size(weight, n)/2
In this step, we get the sparse convex clustering result and put it into the same function ###"create_adjacency" to get the adjacency matrix so that we can get final clustering classes.
system.time(tt <- scvxclust(X = sample_scale, weight, nu = nu, Gamma1 = Gamma1, Gamma2 = Gamma2, Gamma2_weight = Gamma2_weight, method = "ama", max_iter = 1e4, tol_abs = 1e-5))
A <- create_adjacency(tt$V[[1]], weight, 80, method = "ama") cluster <- find_clusters(A) cluster
tt$V[[1]]
Thus the main problem is that we use S-AMA to get a clustering result. But no matter how ###large the value of gamma_1 is set, the final object "cluster" may reflect that every one ###sample belongs to one class, while the AMA result may not fall into such situation.
I find the difference may comes from whether we truncate matrix V, because AMA forms a ###truncated V matrix, while S-AMA not do this.
`