spectral clustering
Data
subject:: Data Science Methods for Large Scale Graphs
parent:: Graph Signals and Graph Signal Processing
theme:: math notes
spectral clustering algorithm
- Diagonalize
- Order the eigenvectors by decreasing eigenvalue magnitude
This yields $$V_{c} = \begin{bmatrix}v_{1} & v_{2} & \dots & v_{c}\end{bmatrix}, v_{i} \in \mathbb{R}^n = \begin{bmatrix}u_{1} \
u_{2} \
\vdots \
u_{n}
\end{bmatrix}, u_{j} \in \mathbb{R}^c$$
we can see the rowsas embeddings of the notes in (community space) - yay we can cluster (
-means or whatever you want. can also use like gaussian mixture models)
This is the unsupervised version of spectral embedding.
See also graph fourier transform
