Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi [2006.13948] Extracting the main trend in a dataset: the Sequencer algorithm 2023-03-21T15:25:36+00:00 https://arxiv.org/abs/2006.13948 cshalizi to:NB dimension_reduction time_series https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c9fd387ee16a/ [2303.04871] Discovering a change point in a time series of organoid networks via the iso-mirror 2023-03-17T18:12:06+00:00 https://arxiv.org/abs/2303.04871 cshalizi to:NB neural_data_analysis network_data_analysis change-point_problem re:network_differences dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5859e41f08a4/ now publishers - Minimum-Distortion Embedding 2021-10-18T13:50:48+00:00 https://nowpublishers.com/article/Details/MAL-090 cshalizi to:NB to_read dimension_reduction principal_components graph_embedding re:hyperbolic_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:828ed2b065ef/ Improving the Measurement of Shared Cultural Schemas with Correlational Class Analysis: Theory and Method | Sociological Science 2021-06-27T18:49:19+00:00 https://sociologicalscience.com/articles-v4-15-353/ cshalizi to:NB surveys dimension_reduction inference_to_latent_objects social_measurement sociology via:gabriel_rossman statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ef8371b87faf/ [2105.02499] SDRcausal: an R package for causal inference based on sufficient dimension reduction 2021-05-13T05:24:55+00:00 https://arxiv.org/abs/2105.02499 cshalizi to:NB causal_inference dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:100741ef7313/ Model Reduction Methods for Complex Network Systems | Annual Review of Control, Robotics, and Autonomous Systems 2021-05-06T13:48:38+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-control-061820-083817 cshalizi to:NB dynamical_systems networks dimension_reduction graph_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b03640a0c4ff/ [1705.00395] Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors 2021-04-22T15:22:57+00:00 https://arxiv.org/abs/1705.00395 cshalizi to:NB prediction time_series factor_analysis dimension_reduction to_read re:your_favorite_dsge_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:595aabfa2cb8/ [2103.06885] Modern Dimension Reduction 2021-03-19T15:54:50+00:00 https://arxiv.org/abs/2103.06885 cshalizi to:NB dimension_reduction to_teach:data-mining via:? https://pinboard.in/ https://pinboard.in/u:cshalizi/b:caee90c5ef8e/ Markov Neighborhood Regression for High-Dimensional Inference: Journal of the American Statistical Association: Vol 0, No 0 2021-01-27T03:40:38+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1841646 cshalizi to:NB linear_regression high-dimensional_statistics dimension_reduction causal_discovery wheels:reinvention_of https://pinboard.in/ https://pinboard.in/u:cshalizi/b:72d2cc24b1b7/ [1401.5226] The Why and How of Nonnegative Matrix Factorization 2021-01-14T16:28:46+00:00 https://arxiv.org/abs/1401.5226 cshalizi to:NB linear_algebra to_teach:data-mining dimension_reduction via:mraginsky https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3172cde1a9f8/ [2101.01535] An RKHS-Based Semiparametric Approach to Nonlinear Sufficient Dimension Reduction 2021-01-06T17:10:25+00:00 https://arxiv.org/abs/2101.01535 cshalizi to:NB nonparametrics hilbert_space dimension_reduction statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4499fc48f267/ The potential of likelihood-free inference of cosmological parameters with weak lensing data | Proceedings of the International Astronomical Union | Cambridge Core 2020-12-13T23:58:29+00:00 https://www.cambridge.org/core/journals/proceedings-of-the-international-astronomical-union/article/potential-of-likelihoodfree-inference-of-cosmological-parameters-with-weak-lensing-data/0E1FEF317A0C09039B52C8791E63670D cshalizi have_read heard_the_talk approved_the_thesis astronomy approximate_bayesian_computation sufficiency exponential_families dimension_reduction simulation-based_estimation statistics in_NB re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:af0f8495eafc/ [1706.06296] Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off 2020-12-04T21:43:57+00:00 https://arxiv.org/abs/1706.06296 cshalizi dimension_reduction kernel_methods random_features principal_components in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:36ef988d8d25/ Small summaries for big data | Knowledge management, databases and data mining | Cambridge University Press 2020-11-30T17:32:10+00:00 https://www.cambridge.org/9781108477444 cshalizi to:NB books:noted random_projections locality-sensitive_hashing dimension_reduction clustering data_mining computational_statistics to_teach:data-mining books:in_library books:have_suggested_to_library downloaded re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8fb436ef1b8f/ Embedding Learning: Journal of the American Statistical Association: Vol 0, No 0 2020-11-20T19:48:08+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1775614 cshalizi to:NB dimension_reduction learning_theory prediction sufficiency https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b907e350efd0/ Kim , Li , Yu , Li : On post dimension reduction statistical inference 2020-11-18T22:42:02+00:00 https://projecteuclid.org/euclid.aos/1594972830 cshalizi to:NB dimension_reduction statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c7934148d63d/ [2011.01379] Detecting direct causality in multivariate time series: A comparative study 2020-11-06T05:05:22+00:00 https://arxiv.org/abs/2011.01379 cshalizi to:NB time_series prediction granger_causality dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:114bfd04861d/ Local dimension reduction of summary statistics for likelihood-free inference | SpringerLink 2020-02-23T15:47:12+00:00 https://link.springer.com/article/10.1007/s11222-019-09905-w cshalizi to:NB approximate_bayesian_computation indirect_inference dimension_reduction sufficiency statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e1ae3a67e467/ [1909.12902] Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs 2019-10-01T17:36:07+00:00 https://arxiv.org/abs/1909.12902 cshalizi to:NB dimension_reduction visual_display_of_quantitative_information statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5414d42b3487/ [1909.12898] Identifying Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach 2019-10-01T15:39:46+00:00 https://arxiv.org/abs/1909.12898 cshalizi to:NB markov_models dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:14ad4038c7f7/ [1909.05097] Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion 2019-09-15T17:23:02+00:00 https://arxiv.org/abs/1909.05097 cshalizi to:NB kernel_methods hilbert_space probability dimension_reduction statistics computational_statistics spectral_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3de6f1991994/ [1909.03093] Solving Interpretable Kernel Dimension Reduction 2019-09-11T15:16:43+00:00 https://arxiv.org/abs/1909.03093 cshalizi to:NB kernel_methods dimension_reduction principal_components to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3222a6b145d7/ [1908.06319] Locally Linear Embedding and fMRI feature selection in psychiatric classification 2019-08-20T14:19:42+00:00 https://arxiv.org/abs/1908.06319 cshalizi 80%) on eleven publicly-available datasets containing healthy controls and patients with either Schizophrenia, Attention-Deficit Hyperactivity Disorder (ADHD), or Autism Spectrum Disorder (ASD). Furthermore, unlike the original fMRI data before or after using Principal Component Analysis (PCA) for artefact reduction, the embedded fMRI furnished significantly better than chance classification (defined as the majority class proportion) on ten of eleven datasets. Interpretation: Locally Linear Embedding appears to be a useful feature extraction procedure that retains important information about patterns of brain activity distinguishing among psychiatric cohorts." --- Last tag is because I plan to teach LLE and this might make a good example or assignment, if I like how it was actually done. --- ETA: It's... not horrible (though the writing is bad and far too pretentious), but not very insightful, and too complicated to make a good teaching example.]]> to:NB locally_linear_embedding classifiers fmri dimension_reduction to_teach:data-mining have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ac687c5e5458/ Dimension reduction for the conditional mean and variance functions in time series - Park - - Scandinavian Journal of Statistics - Wiley Online Library 2019-08-07T17:25:18+00:00 https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12405 cshalizi to:NB prediction time_series dimension_reduction statistics information_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:04b9e80f3a87/ [1905.09944] Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis 2019-05-27T15:01:50+00:00 https://arxiv.org/abs/1905.09944 cshalizi to:NB dimension_reduction time_series statistics information_theory my_initial_skeptical_coloration_became_on_examination_a_permanent_stain https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4964c3d7ceb4/ Optimal dimension reduction for high-dimensional and functional time series | SpringerLink 2018-07-13T02:43:55+00:00 https://link.springer.com/article/10.1007/s11203-018-9172-1 cshalizi to:NB dimension_reduction time_series statistics principal_components https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4d73961cf5ab/ An elementary proof of a theorem of Johnson and Lindenstrauss - Dasgupta - 2003 - Random Structures & Algorithms - Wiley Online Library 2018-05-31T18:39:43+00:00 http://dx.doi.org/10.1002/rsa.10073 cshalizi to:NB random_projections geometry dimension_reduction have_read re:ADAfaEPoV https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b91ed1e17030/ [1802.03426] UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction 2018-03-11T18:56:42+00:00 https://arxiv.org/abs/1802.03426 cshalizi to:NB via:vaguery manifold_learning dimension_reduction data_analysis data_mining to_teach:data-mining re:ADAfaEPoV https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b2108ad9d881/ Predicting tipping points in mutualistic networks through dimension reduction 2018-01-24T23:31:48+00:00 http://www.pnas.org/content/115/4/E639 cshalizi to:NB ecology dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e4b78f526679/ Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities | Neural Computation | MIT Press Journals 2018-01-24T23:29:28+00:00 https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01035 cshalizi to:NB dimension_reduction sufficiency density_estimation linear_regression statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:15bf571a5ee2/ Poincaré Embeddings for Learning Hierarchical Representations 2017-11-24T18:34:20+00:00 http://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations cshalizi to:NB hyperbolic_geometry dimension_reduction hierarchical_structure statistics data_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c2b04267ee36/ Additive Component Analysis – Calvin Murdock 2017-08-08T22:11:50+00:00 http://www.calvinmurdock.com/aca/ cshalizi to:NB dimension_reduction manifold_learning additive_models principal_components statistics to_read re:ADAfaEPoV https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f0476aa38803/ [1602.00531] Adaptive non-parametric estimation in the presence of dependence 2016-02-08T21:30:35+00:00 http://arxiv.org/abs/1602.00531 cshalizi to:NB statistics regression nonparametrics learning_under_dependence density_estimation dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b9b529946e46/ Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays 2015-08-20T13:25:04+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00759 cshalizi to:NB to_read inference_to_latent_objects dimension_reduction neural_data_analysis nonparametrics time_series yu.byron https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6c01bbea454b/ The geometry of graphs and some of its algorithmic applications (Linial, London and Rabinovich, 1995) 2015-07-14T04:02:31+00:00 http://link.springer.com/article/10.1007/BF01200757 cshalizi in_NB graph_theory dimension_reduction mathematics random_projections clustering have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bfbac4c36470/ [1503.00173] Signal Processing on Graphs: Modeling (Causal) Relations in Big Data 2015-05-27T14:21:46+00:00 http://arxiv.org/abs/1503.00173 cshalizi to:NB time_series causal_inference causal_discovery dimension_reduction statistics color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7408f6e854bc/ Feng , He : Statistical inference based on robust low-rank data matrix approximation 2015-02-23T07:17:31+00:00 http://projecteuclid.org/euclid.aos/1392733185 cshalizi to:NB dimension_reduction low-rank_approximation statistics re:g_paper https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9d7b6824a182/ Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis 2014-12-29T02:03:46+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00684 cshalizi to:NB neural_data_analysis time_series dimension_reduction statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:812a98e48913/ [1211.3046] Recovering the Optimal Solution by Dual Random Projection 2014-07-30T16:05:26+00:00 http://arxiv.org/abs/1211.3046 cshalizi to:NB optimization low-rank_approximation dimension_reduction random_projections https://pinboard.in/ https://pinboard.in/u:cshalizi/b:63b8be9dbcf0/ [1406.0873] Unifying linear dimensionality reduction 2014-07-12T00:26:03+00:00 http://arxiv.org/abs/1406.0873 cshalizi data_analysis principal_components factor_analysis optimization statistics dimension_reduction ghahramani.zoubin in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6bae21f8af20/ [0907.0199] High-Dimensional Density Estimation via SCA: An Example in the Modelling of Hurricane Tracks 2014-04-22T15:55:22+00:00 http://arxiv.org/abs/0907.0199 cshalizi to_read heard_the_talk kith_and_kin dimension_reduction buchman.susan high-dimensional_statistics density_estimation meteorology hurricanes diffusion_maps entableted in_NB lee.ann_b. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:feaeb1982195/ Taylor & Francis Online :: Principal Flows - Journal of the American Statistical Association - Volume 109, Issue 505 2014-04-02T01:54:34+00:00 http://www.tandfonline.com/doi/abs/10.1080/01621459.2013.849199#.Uztl8tx_Tuc cshalizi to:NB dimension_reduction principal_components manifold_learning geometry statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ad0f6432b1db/ [1402.0119] Randomized Nonlinear Component Analysis 2014-03-10T01:44:49+00:00 http://arxiv.org/abs/1402.0119 cshalizi to:NB to_read principal_components dimension_reduction statistics to_teach:undergrad-ADA random_features https://pinboard.in/ https://pinboard.in/u:cshalizi/b:79b245b38a8f/ [1401.0267] Transformed sufficient dimension reduction 2014-03-08T21:45:09+00:00 http://arxiv.org/abs/1401.0267 cshalizi dimension_reduction regression sufficiency statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3874b310ccd7/ Vu , Lei : Minimax sparse principal subspace estimation in high dimensions 2014-02-20T22:20:20+00:00 http://projecteuclid.org/euclid.aos/1388545673 cshalizi to:NB principal_components sparsity dimension_reduction kith_and_kin vu.vincent statistics to_read minimax high-dimensional_statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ed0ddaddd49e/ [1401.6978] Sparsistency and Agnostic Inference in Sparse PCA 2014-02-03T20:25:20+00:00 http://arxiv.org/abs/1401.6978 cshalizi to:NB principal_components sparsity dimension_reduction kith_and_kin statistics vu.vincent lei.jing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2a94c1eec2f6/ Guided Learning for Role Discovery (GLRD): Framework, Algorithms, and Applications 2014-01-08T18:21:50+00:00 http://eliassi.org/papers/gilpin-kdd2013.pdf cshalizi to:NB to_read community_discovery network_data_analysis data_mining re:network_differences heard_the_talk matrix_and_tensor_factorization dimension_reduction eliassi-rad.tina https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1dad24d3ed94/ [1310.5089] Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods 2013-10-23T14:25:13+00:00 http://arxiv.org/abs/1310.5089 cshalizi to:NB signal_processing dimension_reduction regression principal_components kernel_methods statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a643506f78fb/ [1306.1350] Diffusion map for clustering fMRI spatial maps extracted by independent component analysis 2013-09-30T20:40:38+00:00 http://arxiv.org/abs/1306.1350 cshalizi to:NB fmri dimension_reduction neuroscience diffusion_maps statistics independent_component_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8e53e7de3081/ [1309.4054] Data-driven Algorithms for Dimension Reduction in Causal Inference: analyzing the effect of school achievements on acute complications of type 1 diabetes mellitus 2013-09-18T00:35:40+00:00 http://arxiv.org/abs/1309.4054 cshalizi to:NB dimension_reduction causal_inference nonparametrics statistics to_teach:undergrad-ADA https://pinboard.in/ https://pinboard.in/u:cshalizi/b:eb3e35141299/ [1309.2895] Sparse and Functional Principal Components Analysis 2013-09-12T20:05:36+00:00 http://arxiv.org/abs/1309.2895 cshalizi to:NB sparsity functional_data_analysis principal_components statistics dimension_reduction allen.genevera_i. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:def8a3768d6a/ Empirical intrinsic geometry for nonlinear modeling and time series filtering 2013-09-03T12:26:55+00:00 http://www.pnas.org/content/110/31/12535.abstract cshalizi time_series prediction manifold_learning dimension_reduction statistics machine_learning to_read filtering state_estimation information_geometry entableted in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5c5423af007e/ [1305.7255] Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery 2013-06-06T17:09:06+00:00 http://arxiv.org/abs/1305.7255 cshalizi manifold_learning statistics in_NB metric_learning dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:59c1108de4e1/ Identifying Functional Bases for Multidimensional Neural Computations 2013-06-01T16:46:41+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00465 cshalizi to:NB neural_networks neural_data_analysis dimension_reduction inverse_problems distributed_systems basis_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a62ae8e915cf/ Blum , Nunes , Prangle , Sisson : A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation 2013-05-21T22:51:55+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1369147911 cshalizi approximate_bayesian_computation statistics dimension_reduction information_criteria in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:55662e045598/ Efficiency loss and the linearity condition in dimension reduction 2013-05-11T16:32:17+00:00 http://biomet.oxfordjournals.org/content/100/2/371.short?rss=1 cshalizi to:NB dimension_reduction regression statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:38a59016a2f7/ Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing 2013-05-07T20:34:13+00:00 http://jmlr.csail.mit.edu/papers/v14/chen13a.html cshalizi manifold_learning dimension_reduction data_mining to_teach:data-mining visual_display_of_quantitative_information network_data_analysis in_NB network_visualization https://pinboard.in/ https://pinboard.in/u:cshalizi/b:82fe59f894b9/ [1304.6487] Locally linear representation for subspace learning and clustering 2013-04-26T00:21:06+00:00 http://arxiv.org/abs/1304.6487 cshalizi to:NB data_mining dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e06f2a3f104c/ [1304.6663] Low-rank optimization for distance matrix completion 2013-04-26T00:19:53+00:00 http://arxiv.org/abs/1304.6663 cshalizi to:NB low-rank_approximation dimension_reduction dimension_estimation high-dimensional_statistics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8487aa08d4fb/ [1304.5802] Nonlinear Basis Pursuit 2013-04-23T18:00:10+00:00 http://arxiv.org/abs/1304.5802 cshalizi to:NB sparsity dimension_reduction information_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:69d447e1a3a2/ Lee , Li , Chiaromonte : A general theory for nonlinear sufficient dimension reduction: Formulation and estimation 2013-03-26T16:44:38+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1364302741 cshalizi sufficiency regression dimension_reduction to_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:42f9fd4fc82f/ Ma , Zhu : Efficient estimation in sufficient dimension reduction 2013-03-26T16:43:50+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1364302742 cshalizi statistics sufficiency dimension_reduction regression in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6f8da75bf795/ Dümbgen, Del Conte-Zerial: On low-dimensional projections of high-dimensional distributions 2013-03-10T01:12:49+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.imsc/1362751182 cshalizi to:NB dimension_reduction high-dimensional_probability probability random_projections https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1fade6af970c/ [1302.2752] Adaptive Metric Dimensionality Reduction 2013-03-06T15:29:12+00:00 http://arxiv.org/abs/1302.2752 cshalizi to:NB dimension_reduction principal_components machine_learning kith_and_kin kontorovich.aryeh https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5968ffe22d74/ Efficient Gaussian process regression for large datasets 2013-02-23T17:46:20+00:00 http://biomet.oxfordjournals.org/content/100/1/75.short?rss=1 cshalizi to:NB gaussian_processes regression nonparametrics statistics compressed_sensing dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:49f5a0eb3f27/ [1302.4881] Elliptical Insights: Understanding Statistical Methods through Elliptical Geometry 2013-02-21T23:40:01+00:00 http://arxiv.org/abs/1302.4881 cshalizi to:NB geometry statistics visual_display_of_quantitative_information dimension_reduction data_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ef5149e48935/ [1002.4283] Learning gradients on manifolds 2013-02-21T23:27:09+00:00 http://arxiv.org/abs/1002.4283 cshalizi dimension_reduction manifold_learning statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4461aef9a54c/ [1104.3472] Principal arc analysis on direct product manifolds 2013-02-21T23:22:14+00:00 http://arxiv.org/abs/1104.3472 cshalizi to:NB statistics dimension_reduction principal_components statistics_on_manifolds https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2bf4e5566e74/ [1205.6040] Nonlinear manifold representations for functional data 2013-02-21T23:14:02+00:00 http://arxiv.org/abs/1205.6040 cshalizi manifold_learning statistics in_NB functional_data_analysis dimension_reduction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f4cdb9c1ee4b/ Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation 2013-02-06T14:42:37+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00407 cshalizi dimension_reduction regression nonparametrics information_theory sufficiency statistics machine_learning in_NB sugiyama.masashi https://pinboard.in/ https://pinboard.in/u:cshalizi/b:34bf4767419a/