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Transformation-Invariant Clustering and Dimensionality Reduction Using EM (2000)  (Make Corrections)  (5 citations)
Brendan Frey, Nebojsa Jojic



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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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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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495   Eigenfaces for recognition (context) - Turk, Pentland - 1991
340   Learning representations by back-propagating errors (context) - Rumelhart, Hinton et al. - 1986
244   Example-based learning for view-based human face detection - Sung, Poggio - 1994
224   Representing moving images with layers - Wang, Adelson - 1994
115   Graphical Models for Machine Learning and Digital Communicat.. (context) - Frey - 1998
106   Efficient pattern recognition using a new transformation dis.. (context) - Simard, Le Cun et al. - 1993
91   Mixture models for optical flow computation - Jepson, Black - 1993
85   The EM algorithm for mixtures of factor analyzers - Ghahramani, Hinton - 1997
75   Gradient-based learning applied to document recognition - Le Cun, Bottou et al. - 1998
73   Tangent prop -- a formalism for specifying selected invarian.. (context) - Simard, Victorri et al. - 1992
66   Smoothness in layers: Motion segmentation using nonparametri.. - Weiss - 1997
64   EM algorithms for ML factor analysis (context) - Rubin, Thayer - 1982

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