# Multiple-dimension scaling (MDS) objective for MDS and PCA

The following is the MDS Objective.

Let's think of a senario where I apply MDS with/from the solution I obtained from PCA. Then I calculate the objective function on the initial PCA solution and MDS solution (after applying MDS on the former PCA solution). Then I would for sure assume that the objective function will decrease for the MDS solution compared with PCA solution. However, when I calculate the objective function respectively, MDS solution yields higher objective function value. Is this normal?

I am attaching my code below:

import os
import pickle
import gzip
import argparse
import time
import matplotlib.pyplot as plt
import numpy as np
from numpy.linalg import norm

from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.preprocessing import LabelBinarizer
from sklearn import decomposition

from neural_net import NeuralNet, stochasticNeuralNet
from manifold import MDS, ISOMAP
import utils

def mds_objective(Z,X):
sum = 0
n,d = Z.shape
for i in range(n):
for j in range(i+1,n):
sum += (norm(Z[i,:]-Z[j,:],2)-norm(X[i,:]-X[j,:],2))**2
return 0.5*sum

X = dataset['X'].astype(float)
animals = dataset['animals']
n, d = X.shape
pca = decomposition.PCA(n_components = 5)
pca.fit(X)
Z = pca.transform(X)
plt.figure()
plt.scatter(Z[:, 0], Z[:, 1])
for i in range(n):
plt.annotate(animals[i], (Z[i,0], Z[i,1]))
utils.savefig('PCA.png')

print(pca.explained_variance_ratio_)
print(mds_objective(Z,X))

X = dataset['X'].astype(float)
animals = dataset['animals']
n,d = X.shape

model = MDS(n_components=2)
Z = model.compress(X)

fig, ax = plt.subplots()
ax.scatter(Z[:,0], Z[:,1])
plt.ylabel('z2')
plt.xlabel('z1')
plt.title('MDS')
for i in range(n):
ax.annotate(animals[i], (Z[i,0], Z[i,1]))
utils.savefig('MDS_animals.png')
print(mds_objective(Z,X))


It prints the following:

1673.1096816455256

1776.8183112784652

• Welcome to AI.SE. What is the point in attaching the code? If you think there might be a bug in the code, then you are out of luck. We do not debug codes. If you are sure your code is correct, then you can post the relevant mathematics and parameters or details and the results.
– user9947
Mar 18, 2020 at 22:07
• I see. I'm not 100% sure if the code is correct. Probably I will have more chances at stack exchange? Mar 18, 2020 at 22:19
• Can't say anything. Data science.SE might be of help, though I am not sure.
– user9947
Mar 18, 2020 at 22:23