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Abstract: Clustering and dimensionality reduction are simple, effective ways to derive useful representations of data, such as images. These procedures often are used as preprocessing steps for more sophisticated pattern analysis techniques. (In fact, these procedures often perform as well as or better than more sophisticated pattern analysis techniques.) However, in situations where each input has been randomly transformed (e.g., by translation, rotation and shearing in images), these methods tend to... (Update)
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.... the appearance basis, the experiments show only translation invariant recognition, as proposed by Black and Jepson [4] Frey and Jojic [21] took a different approach and they introduce an Expectation Maximization (EM) algorithm for factor analysis (similar to PCA) that is...
...etc. so that it can appear with di erent geometry in di erent frames of the video sequence. Following previous work by Frey and Jojic [6], the transformations are represented by a discrete set of possible transformations. For example, single pixel translations in an M N image...
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BibTeX entry: (Update)
B. J. Frey and N. Jojic. Transformation-invariant clustering and dimensionality reduction. Submitted to IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000. http://citeseer.ist.psu.edu/frey00transformationinvariant.html More
@misc{ frey00transformationinvariant,
author = "B. Frey and N. Jojic",
title = "Transformation-invariant clustering and dimensionality reduction",
text = "B. J. Frey and N. Jojic. Transformation-invariant clustering and dimensionality
reduction. Submitted to IEEE Transaction on Pattern Analysis and Machine
Intelligence, 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/frey00transformationinvariant.html" }
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