A random walk is known as a random process which describes a path including a
succession of random steps in the mathematical space. It has increasingly been popular in …

As recently as the 1990s, studies showed that most people preferred getting information
from other people rather than from information retrieval systems. Of course, in that time …

We present a novel clustering method using the approach of support vector machines. Data
points are mapped by means of a Gaussian kernel to a high dimensional feature space …

A central problem in data analysis is the low dimensional representation of high dimensional
data and the concise description of its underlying geometry and density. In the analysis of …

To classify a large number of unlabeled examples we combine a limited number of labeled
examples with a Markov random walk representation over the unlabeled examples. The …

This paper presents a diffusion based probabilistic interpretation of spectral clustering and
dimensionality reduction algorithms that use the eigenvectors of the normalized graph …

Information bottleneck (IB) is a technique for extracting information in one random variable X
that is relevant for predicting another random variable Y. IB works by encoding X in a …

Search engines can record which documents were clicked for which query, and use these
query-document pairs as" soft" relevance judgments. However, compared to the true …

We present a simple and scalable graph clustering method called power iteration clustering
(PIC). PIC finds a very low-dimensional embedding of a dataset using truncated power …

Spectral clustering refers to a class of techniques which rely on the eigenstructure of a
similarity matrix to partition points into disjoint clusters, with points in the same cluster having …