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Principal component analysis
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#	Item
11	Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization Canyi Lu1 , Jiashi Feng1 , Yudong Chen2 , Wei Liu3 , Zhouchen Lin4,5,∗, Shuicheng Yan6,1 1 2 Add to Reading List Source URL: www.cis.pku.edu.cn - Date: 2016-10-19 02:44:00
12	Linear Dimensionality Reduction: Principal Component Analysis Piyush Rai Machine Learning (CS771A) Sept 2, 2016 Add to Reading List Source URL: cse.iitk.ac.in - Date: 2016-09-03 04:33:30
13	Deriving numerical abstract domains via Principal Component Analysis Gianluca Amato, Maurizio Parton, and Francesca Scozzari Universit` a di Chieti-Pescara – Dipartimento di Scienze Add to Reading List Source URL: www.sci.unich.it - Date: 2013-12-10 01:36:05
14	The theoretical justifications for Principal Component Analysis (PCA) typically assume that the data is IID over the estimation window. In practice, this assumption is routinely violated in financial data. We examine the Add to Reading List Source URL: mmds-data.org - Date: 2016-06-23 15:50:48
15	– the FoM for “rock” appears to have become very poor now. • Combining all feature dimensions from acoustic information below 20 Hz and above 4186 Hz. – (Rock recovers partly) 3.3MUSIC FilteringCONTENT Add to Reading List Source URL: wp.nyu.edu Language: English - Date: 2016-08-16 16:20:31 Algebra Physics Statistics Linear algebra Multivariate statistics Statistical classification Support vector machine Machine learning Euclidean vector Principal component analysis Light scattering Scattering
16	Knowl Inf Syst DOIs10115REGULAR PAPER Density-preserving projections for large-scale local anomaly detection Add to Reading List Source URL: pmg.it.usyd.edu.au Language: English - Date: 2011-10-04 00:04:53 Statistics Dimension reduction Data mining Multivariate statistics Robust statistics Machine learning Outlier Local outlier factor Anomaly detection Principal component analysis Random sample consensus Dimensionality reduction
17	To appear in Neural Computation Methods for Binary Multidimensional Scaling Douglas L. T. Rohde School of Computer Science, Carnegie Mellon University, and the Center for the Neural Basis of Cognition Add to Reading List Source URL: tedlab.mit.edu Language: English - Date: 2012-08-07 12:32:37 Algebra Mathematics Linear algebra Vectors Multivariate statistics Matrix theory Abstract algebra Multidimensional scaling Singular value decomposition Euclidean vector Principal component analysis Latent semantic analysis
18	7.2 STATISTICAL FORECASTS OF WESTERN WILDFIRE SEASON SEVERITY 1, 1 Add to Reading List Source URL: ulmo.ucmerced.edu Language: English - Date: 2009-09-24 14:04:55 Statistics Prediction Statistical forecasting Multivariate statistics Covariance and correlation Estimation theory Time series analysis Canonical correlation Wildfire Principal component analysis Forecasting G factor
19	Genome Informatics 13: 112–Marginalized Kernels for RNA Sequence Data Analysis Taishin Kin Add to Reading List Source URL: www.jsbi.org Language: English - Date: 2002-12-09 05:53:39 Algebra Mathematics Linear algebra Multivariate statistics Hilbert space Operator theory Geostatistics Kernel method Transfer RNA Kernel principal component analysis Positive-definite kernel Kernel
20	WORKING PAPER N° How important is innovation? A Bayesian factor-augmented productivity model on panel data Add to Reading List Source URL: www.tepp.eu Language: English - Date: 2015-07-01 09:59:09 Statistics Mathematics Multivariate statistics Number theory Normal distribution Distribution Factor analysis Principal component analysis

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