Description: em algorithm - Find approximate solution to Sf = conv(s,f) = d using EM iteration.
EM seeks to minimize the Poisson negative log likelihood function
J(f) = sum_i {[Sf]_i - (d_i + sigma^2)*log([Sf]_i + sigma^2)}.
To Search:
File list (Check if you may need any files):
MDL_segmenter\coding_length.m
.............\coding_length_v1.m
.............\coding_length_v2.m
.............\gaussian2D.tif
.............\MDL_segmentor.asv
.............\MDL_segmentor.m
.............\subspaces3D.tif
.............\test_MDL_segmentor.asv
.............\test_MDL_segmentor.m
.............\helper_functions
.............\................\.DS_Store
.............\................\._.DS_Store
.............\................\arrow.m
.............\................\arrow3.m
.............\................\average_basis_error.m
.............\................\balls_and_bins.m
.............\................\cluster_subspaces.m
.............\................\EM_subspace.m
.............\................\find_good_sample.m
.............\................\generate_group_dimension_configurations.m
.............\................\generate_hilbert_constraint_entry.m
.............\................\generate_image_noise.m
.............\................\generate_samples.asv
.............\................\generate_samples.m
.............\................\generate_samples_for_hilbert_constraint.m
.............\................\generate_subsets.m
.............\................\generate_veronese_maps.m
.............\................\hat.m
.............\................\hypersphere_area.m
.............\................\hypersphere_volume.m
.............\................\intersection_reclassification.m
.............\................\Ksubspaces.m
.............\................\K_means_subspaces_fixed_dimension.m
.............\................\mahalanobis_distance.m
.............\................\mst.m
.............\................\normalize.m
.............\................\normalize_variance.m
.............\................\PCA_downsampler.m
.............\................\plot_data.m
.............\................\plot_level_plane.m
.............\................\plot_normals.m
.............\................\plot_polynomial.m
.............\................\plot_subspaces.m
.............\................\plot_subspaces2.m
.............\................\plot_subspaces3.asv
.............\................\plot_subspaces3.m
.............\................\plot_subspaces4.m
.............\................\plot_unit_sphere.m
.............\................\point_to_space_distance.m
.............\................\rand_hypersphere.m
.............\................\rand_special_orthogonal.m
.............\................\rand_uniform_inside_hypersphere.m
.............\................\rand_uniform_on_hypersphere.m
.............\................\relabel_samples.m
.............\................\relabel_samples_greedy.asv
.............\................\relabel_samples_greedy.m
.............\................\stereo_view.m
.............\................\subspace_angle.m
.............\................\subspace_bases.m
.............\................\subspace_voting.m
.............\................\test_EM_subspaces.m
.............\................\test_hypersphere_area.m
.............\................\test_hypersphere_volume.m
.............\................\test_Ksubspaces.m
.............\................\test_normalize_variation.m
.............\................\test_rand_special_orthogonal.m
.............\................\test_rand_uniform_inside_hypersphere.m
.............\................\test_rand_uniform_on_hypersphere.m
.............\................\test_rgpca_leave_one_out.m
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