# Clustering, dimensionality reduction, and side information

@inproceedings{Jain2006ClusteringDR, title={Clustering, dimensionality reduction, and side information}, author={Anil K. Jain and H. M. C. Law}, year={2006} }

- Published 2006

Recent advances in sensing and storage technology have created many high-volume, high-dimensional data sets in pattern recognition, machine learning, and data mining. Unsupervised learning can provide generic tools for analyzing and summarizing these data sets when there is no well-defined notion of classes. The purpose of this thesis is to study some of the open problems in two main areas of unsupervised learning, namely clustering and (unsupervised) dimensionality reduction. Instance-level… CONTINUE READING

#### Citations

##### Publications citing this paper.

SHOWING 1-10 OF 12 CITATIONS

## Video sequence alignment

VIEW 4 EXCERPTS

CITES METHODS & BACKGROUND

HIGHLY INFLUENCED

## Genetic-guided semi-supervised clustering algorithm with instance-level constraints

VIEW 6 EXCERPTS

CITES BACKGROUND & METHODS

HIGHLY INFLUENCED

## Automated caries detection based on Radon transformation and DCT

VIEW 1 EXCERPT

CITES METHODS

## A Two layer semi-supervised Clustering method for text retrieval

VIEW 1 EXCERPT

CITES METHODS

## Low-rank kernel learning for semi-supervised clustering

VIEW 1 EXCERPT

CITES BACKGROUND

## Search and retrieval in massive data collections

VIEW 1 EXCERPT

CITES BACKGROUND

## A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification

VIEW 1 EXCERPT

CITES BACKGROUND

## Recovery Rate of Clustering Algorithms

VIEW 1 EXCERPT

CITES BACKGROUND

## About the Calculation of Upper Bounds for Cluster Recovery Rates

VIEW 1 EXCERPT

CITES BACKGROUND

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 282 REFERENCES

## Nonlinear Component Analysis as a Kernel Eigenvalue Problem

VIEW 10 EXCERPTS

HIGHLY INFLUENTIAL

## Semi-supervised graph clustering: a kernel approach

VIEW 8 EXCERPTS

HIGHLY INFLUENTIAL

## A probabilistic framework for semi-supervised clustering

VIEW 10 EXCERPTS

HIGHLY INFLUENTIAL

## Segmentation given partial grouping constraints

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## Global versus local approaches to nonlinear dimensionality reduction

VIEW 7 EXCERPTS

HIGHLY INFLUENTIAL

## Isometric Embedding and Continuum ISOMAP

VIEW 14 EXCERPTS

HIGHLY INFLUENTIAL

## Automatic Alignment of Local Representations

VIEW 6 EXCERPTS

HIGHLY INFLUENTIAL

## Charting a Manifold

VIEW 6 EXCERPTS

HIGHLY INFLUENTIAL

## From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

VIEW 6 EXCERPTS

HIGHLY INFLUENTIAL