Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi now publishers - Universal Features for High-Dimensional Learning and Inference 2025-03-20T13:03:17+00:00 https://www.nowpublishers.com/article/Details/CIT-107 cshalizi to:NB information_theory pattern_discovery information_bottleneck principal_components low-dimensional_summaries information_geometry collaborative_filtering https://pinboard.in/ https://pinboard.in/u:cshalizi/b:62fffa70f391/ Linear Diffusion: Building a Diffusion Model from linear Components — Count Bayesie 2024-11-28T00:35:00+00:00 https://www.countbayesie.com/blog/2023/4/21/linear-diffusion cshalizi have_read data_mining principal_components generative_diffusion_models in_NB to_teach:statistics_and_generative_ai https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6ea3912b2b43/ 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/ [1809.08771] Modeling longitudinal data using matrix completion 2021-08-06T15:49:39+00:00 https://arxiv.org/abs/1809.08771 cshalizi to:NB to_read time_series missing_data principal_components hastie.trevor to_teach:data_over_space_and_time statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:eac3788e7dd6/ [2107.13894] Inference in heavy-tailed non-stationary multivariate time series 2021-07-30T02:55:07+00:00 https://arxiv.org/abs/2107.13894 cshalizi to:NB time_series heavy_tails principal_components non-stationarity statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3d5a2458f453/ [2106.14238] Interpretable Network Representation Learning with Principal Component Analysis 2021-06-30T02:50:06+00:00 https://arxiv.org/abs/2106.14238 cshalizi to:NB network_data_analysis principal_components to_read statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4951d6ca8b4e/ [2105.08875] Statistical Optimality and Computational Efficiency of Nyström Kernel PCA 2021-05-30T20:41:37+00:00 https://arxiv.org/abs/2105.08875 cshalizi to:NB kernel_methods principal_components computational_statistics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2c11c0e72152/ Polygenic Scores: How well can we separate genetics from the environment? | eLife 2021-01-14T18:47:24+00:00 https://elifesciences.org/articles/64948 cshalizito:NB human_genetics principal_components https://pinboard.in/ https://pinboard.in/u:cshalizi/b:15e691be4d20/ Demographic history mediates the effect of stratification on polygenic scores | eLife 2021-01-14T18:46:27+00:00 https://elifesciences.org/articles/61548 cshalizi to:NB human_genetics principal_components re:g_paper https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e67b4648fc6b/ [2101.01839] Generalized Stochastic Processes as Linear Transformations of White Noise 2021-01-07T21:48:55+00:00 https://arxiv.org/abs/2101.01839 cshalizi to:NB stochastic_processes principal_components https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3bf2fb8a28d4/ [2012.09141] Change Detection: A functional analysis perspective 2020-12-17T15:28:45+00:00 https://arxiv.org/abs/2012.09141 cshalizi to:NB change-point_problem anomaly_detection principal_components stochastic_processes statistical_inference_for_stochastic_processes statistics spatio-temporal_statistics spatial_statistics time_series hilbert_space https://pinboard.in/ https://pinboard.in/u:cshalizi/b:20d5ce90dc91/ [2006.07691] Synthetic Interventions 2020-12-15T13:00:55+00:00 https://arxiv.org/abs/2006.07691 cshalizi to:NB time_series principal_components factor_analysis causal_inference statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:166f91ae66b1/ [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/ Demographic history impacts stratification in polygenic scores | bioRxiv 2020-11-19T20:15:25+00:00 https://www.biorxiv.org/content/10.1101/2020.07.20.212530v1 cshalizi to:NB genomics statistics prediction principal_components re:g_paper historical_genetics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cb27d39ae079/ Barigozzi , Cho : Consistent estimation of high-dimensional factor models when the factor number is over-estimated 2020-11-16T16:23:26+00:00 https://projecteuclid.org/euclid.ejs/1597197614 cshalizi time_series factor_analysis high-dimensional_statistics inference_to_latent_objects statistics principal_components in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6a8174061a91/ Jones , Artemiou , Li : On the predictive potential of kernel principal components 2020-11-16T16:19:36+00:00 https://projecteuclid.org/euclid.ejs/1578020612 cshalizi to:NB to_read principal_components statistics prediction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2b4141a9695d/ [1910.06517] Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG 2019-10-16T15:20:27+00:00 https://arxiv.org/abs/1910.06517 cshalizi to:NB principal_components linear_regression computational_statistics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b4041061a276/ The Style Maven Astrophysicists of Silicon Valley | WIRED 2019-10-10T20:39:10+00:00 https://www.wired.com/story/the-style-maven-astrophysicists-of-silicon-valley/ cshalizi have_read data_mining physics principal_components utter_stupidity singular_value_decomposition_rules_everything_around_me to_teach:data-mining fashion https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5a5beb3353d4/ [1905.11926] Network Deconvolution 2019-10-01T16:39:15+00:00 https://arxiv.org/abs/1905.11926 cshalizi to:NB neural_networks statistics principal_components karl_pearson_died_for_your_sins your_favorite_deep_neural_network_sucks to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6d139c0cc67c/ [1909.10787] High-probability bounds for the reconstruction error of PCA 2019-09-26T18:01:25+00:00 https://arxiv.org/abs/1909.10787 cshalizi to:NB principal_components learning_theory to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e2344a55e38e/ [1909.03681] Outlier Detection in High Dimensional Data 2019-09-15T14:24:42+00:00 https://arxiv.org/abs/1909.03681 cshalizi to:NB anomaly_detection density_estimation principal_components high-dimensional_statistics statistics to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c4ca0118845f/ [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/ PsyArXiv Preprints | A “Need for Chaos” and the Sharing of Hostile Political Rumors in Advanced Democracies 2019-09-08T01:07:54+00:00 https://psyarxiv.com/6m4ts/ cshalizi to:NB us_politics principal_components psychometrics #include:my_usual_skepticism_about_this_kind_of_psychometrics to_teach:data-mining epidemiology_of_representations social_media natural_history_of_truthiness re:actually-dr-internet-is-the-name-of-the-monsters-creator color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0128253782dd/ [1908.02029] Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored Projections 2019-08-07T12:32:40+00:00 https://arxiv.org/abs/1908.02029 cshalizi to:NB anomaly_detection change-point_problem principal_components statistics data_mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:df424a0256a1/ Phys. Rev. E 99, 063304 (2019) - Deriving the order parameters of a spin-glass model using principal component analysis 2019-06-14T12:38:10+00:00 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.063304 cshalizi to:NB principal_components statistical_mechanics phase_transitions https://pinboard.in/ https://pinboard.in/u:cshalizi/b:09d313c48f94/ [1712.05630] Sparse principal component analysis via random projections 2018-09-13T16:40:16+00:00 https://arxiv.org/abs/1712.05630 cshalizi to:NB principal_components sparsity random_projections statistics samworth.richard_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6c614249e1dd/ Analysis of a complex of statistical variables into principal components. 2018-09-08T18:52:35+00:00 http://dx.doi.org/10.1037/h0071325 cshalizi to:NB have_read principal_components data_analysis hotelling.harold re:ADAfaEPoV https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2e89a74cb6f8/ On lines and planes of closest fit to systems of points in space (K. Pearson, 1901) 2018-09-08T18:49:55+00:00 https://www.tandfonline.com/doi/abs/10.1080/14786440109462720 cshalizi to:NB principal_components data_analysis pearson.karl re:ADAfaEPoV have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:79b5a2fdcf75/ 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/ Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization 2018-01-09T20:46:25+00:00 http://www.pnas.org/content/115/2/E144.abstract.html cshalizi to:NB to_read comparative_history complexity_measures principal_components to_teach:undergrad-ADA color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fc1b60107b6e/ 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/ Cat Basis Pursuit 2016-02-24T20:40:33+00:00 http://www.oneweirdkerneltrick.com/catbasis.pdf cshalizi machine_learning cats funny:geeky principal_components sparsity https://pinboard.in/ https://pinboard.in/u:cshalizi/b:031d454cc51b/ Eigenstyle 2015-08-19T16:20:12+00:00 http://graceavery.com/principalcomponentanalysisandfashion/ cshalizi data_mining principal_components fashion have_read to:blog to_teach:data-mining re:ADAfaEPoV via:absfac https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4f7450d5ea6c/ [1407.4578] Maximal Autocorrelation Functions in Functional Data Analysis 2015-01-23T13:27:57+00:00 http://arxiv.org/abs/1407.4578 cshalizi have_skimmed functional_data_analysis principal_components factor_analysis smoothing statistics hooker.giles to_teach:undergrad-ADA in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8e83dc7a7faf/ [1411.4681] Functional Principal Components Analysis of Spatially Correlated Data 2015-01-20T02:35:13+00:00 http://arxiv.org/abs/1411.4681 cshalizi to:NB functional_data_analysis spatial_statistics principal_components statistics hooker.giles https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4cd24bb63924/ [1410.8260] Selecting the number of principal components: estimation of the true rank of a noisy matrix 2014-12-02T01:14:12+00:00 http://arxiv.org/abs/1410.8260 cshalizi to:NB principal_components model_selection statistics tibshirani.robert https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fc0cabb54bff/ "Derivation of theory by means of factor analysis or Tom Swift and his electric factor analysis machine" 2014-08-28T13:25:44+00:00 http://repository.upenn.edu/marketing_papers/13/ cshalizihave_read factor_analysis statistics model_discovery re:g_paper principal_components in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6ae62e192388/ [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/ [1404.0788] On the principal components of sample covariance matrices 2014-04-20T18:32:08+00:00 http://arxiv.org/abs/1404.0788 cshalizi random_matrices principal_components in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bf34974fb041/ Low-Rank Spectral Learning | AISTATS 2014 | JMLR W&CP 2014-04-20T17:47:44+00:00 http://jmlr.org/proceedings/papers/v33/kulesza14.html cshalizi to:NB spectral_methods low-rank_approximation statistics computational_statistics principal_components re:AoS_project singh.satinder_baveja https://pinboard.in/ https://pinboard.in/u:cshalizi/b:273a202b361f/ 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/ 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/ The World is not always Flat or Learning Curved Manifolds 2014-02-19T23:44:28+00:00 http://www.cs.huji.ac.il/~werman/Papers/cmds.pdf cshalizi to_read hyperbolic_geometry manifold_learning multidimensional_scaling re:smoothing_adjacency_matrices principal_components in_NB graph_embedding re:hyperbolic_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bf83be8b8be0/ Subspace clustering of high-dimensional data: a predictive approach - Springer 2014-02-19T02:46:07+00:00 http://link.springer.com/article/10.1007/s10618-013-0317-y cshalizi to:NB low-rank_approximation principal_components clustering data_mining statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e61894d2a58c/ [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/ [1312.1801] Visualizing genetic constraints 2014-01-02T18:35:18+00:00 http://arxiv.org/abs/1312.1801 cshalizi to:NB genetics principal_components visual_display_of_quantitative_information https://pinboard.in/ https://pinboard.in/u:cshalizi/b:164317fa5ea2/ [1211.2671] A General Framework For Consistency of Principal Component Analysis 2013-11-14T04:51:42+00:00 http://arxiv.org/abs/1211.2671 cshalizi to:NB principal_components statistics estimation high-dimensional_statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c83fea219731/ [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/ [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/ A Risk Comparison of Ordinary Least Squares vs Ridge Regression 2013-07-20T13:11:25+00:00 http://jmlr.org/papers/v14/dhillon13a.html cshalizi regression statistics learning_theory principal_components to_teach:undergrad-ADA kakade.sham have_read high-dimensional_statistics foster.dean_p. to_teach:data-mining in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:58ee6967d573/ [1307.0164] Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models 2013-07-02T03:15:45+00:00 http://arxiv.org/abs/1307.0164 cshalizi to:NB time_series principal_components high-dimensional_statistics liu.han statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:237266a4fefe/ Gaydos , Heckman , Kirkpatrick , Stinchcombe , Schmitt , Kingsolver , Marron : Visualizing genetic constraints 2013-06-27T15:13:12+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1372338471 cshalizi to:NB principal_components genetics visual_display_of_quantitative_information data_analysis to_teach:undergrad-ADA https://pinboard.in/ https://pinboard.in/u:cshalizi/b:76215827f891/ [1305.5646] A simple proof for the multivariate Chebyshev inequality 2013-05-27T12:07:39+00:00 http://arxiv.org/abs/1305.5646 cshalizi probability principal_components have_read to_teach:undergrad-ADA in_NB deviation_inequalities https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1dddb31a0283/ Ma : Sparse principal component analysis and iterative thresholding 2013-05-08T14:10:30+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1368018173 cshalizi to:NB sparsity principal_components statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:88b46fa0663f/ Fast and Robust Bootstrap for Multivariate Inference: The R Package FRB 2013-04-22T17:18:58+00:00 http://www.jstatsoft.org/v53/i03 cshalizi principal_components bootstrap statistics robust_statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4d0eb3e5de5c/ [1302.6102] Functional Data Analysis with Increasing Number of Projections 2013-03-06T15:38:08+00:00 http://arxiv.org/abs/1302.6102 cshalizi to:NB principal_components functional_data_analysis statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8c3b7f4dc4d9/ [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/ [1302.1232] When are the most informative components for inference also the principal components? 2013-03-06T14:59:24+00:00 http://arxiv.org/abs/1302.1232 cshalizi to:NB principal_components statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2848db85aac3/ [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/ [1111.4601] Non-Asymptotic Analysis of Tangent Space Perturbation 2013-02-21T23:19:29+00:00 http://arxiv.org/abs/1111.4601 cshalizi statistics manifold_learning principal_components in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2411b784b629/ [1301.0554] Tree-dependent Component Analysis 2013-01-07T22:48:13+00:00 http://arxiv.org/abs/1301.0554 cshalizi to:NB dimension_reduction principal_components graphical_models jordan.michael_i. bach.francis_r. density_estimation chow-liu_trees machine_learning statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:467d041d3e7c/ Principal Cumulant Component Analysis (Jason Morton, Lek-Heng Lim) [pdf] 2013-01-03T01:45:05+00:00 http://www.math.psu.edu/morton/publications/pcca.pdf cshalizi to:NB have_read statistics data_analysis cumulants via:arsyed principal_components to_teach:data-mining to_teach:undergrad-ADA dimension_reduction https://pinboard.in/u:cshalizi/b:a056b61da10e/ Point-Process Principal Components Analysis via Geometric Optimization 2012-12-08T14:16:08+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00382 cshalizi point_processes time_series data_analysis principal_components solo.victor statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:56cdd211d1e8/ [1211.5803] Fast network community detection by SCORE 2012-11-28T14:24:50+00:00 http://arxiv.org/abs/1211.5803 cshalizi network_data_analysis community_discovery principal_components statistics jin.jiashun to_read kith_and_kin spectral_clustering in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2efc3caec26c/ [1207.2812] Near-Optimal Algorithms for Differentially-Private Principal Components 2012-07-13T14:14:01+00:00 http://arxiv.org/abs/1207.2812 cshalizi to:NB principal_components dimension_reduction differential_privacy data_mining sarwate.anand https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6a80c9e5d592/ [1206.4628] Robust PCA in High-dimension: A Deterministic Approach 2012-06-23T13:51:59+00:00 http://arxiv.org/abs/1206.4628 cshalizi to:NB statistics principal_components high-dimensional_statistics robustness https://pinboard.in/ https://pinboard.in/u:cshalizi/b:354f23a07d65/ [1206.4608] A Hybrid Algorithm for Convex Semidefinite Optimization 2012-06-23T13:45:49+00:00 http://arxiv.org/abs/1206.4608 cshalizi optimization machine_learning low-rank_approximation principal_components sparsity in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:14c0d9f5d1ac/ [1205.7060] Sparse Principal Component Analysis with missing observations 2012-06-07T15:54:46+00:00 http://arxiv.org/abs/1205.7060 cshalizi to:NB dimension_reduction missing_data sparsity principal_components statistics data_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:940759f8ce3e/ Phys. Rev. Lett. 108, 200601 (2012): Number of Relevant Directions in Principal Component Analysis and Wishart Random Matrices 2012-05-20T20:34:49+00:00 http://prl.aps.org/abstract/PRL/v108/i20/e200601 cshalizi to_read principal_components large_deviations random_matrices stochastic_processes high-dimensional_probability re:g_paper phase_transitions in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9f2f705f6a93/