Answer true or false and give justification. 1. k-means is a linear dimension reduction technique. 2....

Answer true or false and give justification.

1. k-means is a linear dimension reduction technique.

2. Kernel PCA is a linear dimension reduction technique.

3. Spectral Clustering is a non-linear dimension reduction technique.

Expert Solution

1. k-means is a linear dimension reduction technique.: TRUE

Yes, it is a linear dimension reduction technique because in K means clustering method, the points that lie close to each other fall in the same cluster and are compact around the cluster center. The closeness can be measured by distance between the observations or Centroids.

2. Kernel PCA is a linear dimension reduction technique.: False

Kernel PCA is Nonlinearize a linear dimensionality reduction method hence it is non-linear dimension reduction technique.

3. Spectral Clustering is a non-linear dimension reduction technique:True

Yes, it is a non linear dimension reduction technique because in Spectrum clustering method,Points that are connected or immediately next to each other are put in the same cluster. Even if the distance between 2 points is less, if they are not connected, they are not clustered together

orchestra answered 2 years ago

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