DR+Clustering

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==Data==
==Data==
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MNIST Digits data:
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1. MNIST Digits data
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2. Gisette
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3. Olivetti Faces
==Leader Board==
==Leader Board==
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{|  border="1" style="width: 100%; text-align:left" class="content"
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! Data || # Data points || # Dimensions || # Target Dimensions || Dimensionality Reduction Method || Clustering Technique || Rand Index || NMI || Accuracy  
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! Data || # Data points || # Dimensions || Team Name || # Target Dimensions || Dimensionality Reduction Method || Clustering Technique || Rand Index || NMI || Accuracy  
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| MNIST || || || || || || || ||  
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Revision as of 06:30, 5 October 2012

Contents

CS 6150: Graduate Algorithms Project

High dimensions are "weird".

A mathematician and his best friend, an engineer, attend a public lecture on geometry in thirteen-dimensional space.

"How did you like it?" the mathematician wants to know after the talk.

"My head's spinning", the engineer confesses. "How can you develop any intuition for thirteen-dimensional space?"

"Well, it's not even difficult. All I do is visualize the situation in arbitrary N-dimensional space and then set N = 13."


And Clustering is "hard" Athough Amit Daniely, Nati Linial, Michael Saks say its only hard when it does not matter!)

Goal

Understand the impact of dimensionality reduction methods on clustering. Try to uncover relationship between a dimensionality reduction method and a clustering technique of your choice (if there exists any).

Data

1. MNIST Digits data 2. Gisette 3. Olivetti Faces

Leader Board

Data # Data points # Dimensions Team Name # Target Dimensions Dimensionality Reduction Method Clustering Technique Rand Index NMI Accuracy
MNIST
Gisette
Olivetti Faces
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