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@inproceedings{bb153102,
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        TITLE = "Approximate Log-Hilbert-Schmidt Distances between Covariance
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@inproceedings{bb153103,
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        TITLE = "Convex clustering and recovery of partially observed data",
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@inproceedings{bb153104,
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@inproceedings{bb153105,
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@inproceedings{bb153106,
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        TITLE = "An Efficient Algorithm for Learning Distances that Obey the Triangle
Inequality",
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@inproceedings{bb153107,
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@inproceedings{bb153108,
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@inproceedings{bb153109,
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        TITLE = "Scaling up discrete distribution clustering using ADMM",
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@inproceedings{bb153110,
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        TITLE = "Proximity Clustering for Revealing a Semantically Dominant Class",
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@inproceedings{bb153111,
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@inproceedings{bb153112,
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@inproceedings{bb153113,
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        TITLE = "Discriminative Embedding via Image-to-Class Distances",
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@inproceedings{bb153114,
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@inproceedings{bb153117,
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        TITLE = "A Linear Max K-min classifier",
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@inproceedings{bb153118,
        AUTHOR = "Gu, Z.H. and Shao, M. and Li, L.Y. and Fu, Y.",
        TITLE = "Discriminative metric: Schatten norm vs. vector norm",
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@inproceedings{bb153119,
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        TITLE = "A New Index Based on Sparsity Measures for Comparing Fuzzy Partitions",
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@inproceedings{bb153120,
        AUTHOR = "Wang, Z.X. and Gao, S.H. and Chia, L.T.",
        TITLE = "Learning Class-to-Image Distance via Large Margin and L1-Norm
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@inproceedings{bb153121,
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        PAGES = "VI: 29-42",
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@inproceedings{bb153122,
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        TITLE = "Graph Degree Linkage: Agglomerative Clustering on a Directed Graph",
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@inproceedings{bb153123,
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        TITLE = "New optimization scheme for L2-norm total variation semi-supervised
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@inproceedings{bb153124,
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        TITLE = "Using Ripley's K-function to improve graph-based clustering techniques",
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@inproceedings{bb153125,
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@inproceedings{bb153126,
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        TITLE = "Error-correcting semi-supervised learning with mode-filter on graphs",
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@inproceedings{bb153128,
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        TITLE = "Probabilistic Clustering Using the Baum-Eagon Inequality",
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@inproceedings{bb153129,
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        TITLE = "Margin Preserved Approximate Convex Hulls for Classification",
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@inproceedings{bb153130,
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        TITLE = "A novel image classification method based on manifold learning and
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@inproceedings{bb153131,
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        TITLE = "Distance Learning Based on Convex Clustering",
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@inproceedings{bb153132,
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@inproceedings{bb153133,
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@inproceedings{bb153135,
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        TITLE = "Local Regularization for Multiclass Classification Facing Significant
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@inproceedings{bb153136,
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        TITLE = "Enhanced Visual Separation of Clusters by M-Mapping to Facilitate
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@inproceedings{bb153138,
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@inproceedings{bb153139,
        AUTHOR = "Yamashita, Y. and Numakami, M. and Inoue, N.",
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@inproceedings{bb153140,
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        TITLE = "Parameterless Isomap with Adaptive Neighborhood Selection",
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@inproceedings{bb153141,
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@inproceedings{bb153142,
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        TITLE = "Alternative Approaches and Algorithms for Classification",
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@inproceedings{bb153143,
        AUTHOR = "Demirkol, A. and Demir, Z. and Emre, E.",
        TITLE = "Estimation of Target Density Functions by a New Algorithm",
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@inproceedings{bb153144,
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@inproceedings{bb153145,
        AUTHOR = "Wang, J.G. and Neskovic, P. and Cooper, L.N.",
        TITLE = "A Minimum Sphere Covering Approach to Pattern Classification",
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@inproceedings{bb153146,
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        TITLE = "A New Simplified Gravitational Clustering Method for Multi-prototype
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@inproceedings{bb153147,
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@inproceedings{bb153148,
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@inproceedings{bb153150,
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@inproceedings{bb153151,
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@inproceedings{bb153152,
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@inproceedings{bb153153,
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@inproceedings{bb153154,
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@inproceedings{bb153159,
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@inproceedings{bb153160,
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@inproceedings{bb153161,
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@inproceedings{bb153162,
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@inproceedings{bb153163,
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@inproceedings{bb153164,
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