Live briefing
Everything about Sparse PCA
Statistical analysis technique
Sparse principal component analysis is a technique used in statistical analysis and, in particular, in the analysis of multivariate data sets. It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by introducing sparsity structures to the input variables. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Sparse PCAA 5-minute orientation
- Skim a recent paper: Self-Learning Sparse PCA for Multimode Process MonitoringInfluential work since 2022
- Pick up a classic book: Communications, Signal Processing, and SystemsThe most-reprinted book on the subject
- Read the landmark paper: A Direct Formulation for Sparse PCA Using Semidefinite ProgrammingCited 461 times
- Go sideways: Low-rank approximationA closely related topic
- Go sideways: Principal component regressionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A Direct Formulation for Sparse PCA Using Semidefinite Programming
Sparse PCA: Optimal rates and adaptive estimation
Improve robustness of sparse PCA by L1-norm maximization
Minimax bounds for sparse PCA with noisy high-dimensional data
Convex Sparse PCA for Unsupervised Feature Learning
Two-Dimensional Quaternion PCA and Sparse PCA
Joint Group Sparse PCA for Compressed Hyperspectral Imaging
Sparse PCA for High-Dimensional Data With Outliers
🆕 Influential since 2022
Self-Learning Sparse PCA for Multimode Process Monitoring
Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network
Subexponential-Time Algorithms for Sparse PCA
A Hybrid Model for GRU Ultra-Short-Term Wind Speed Prediction Based on Tsfresh and Sparse PCA
Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique
📚 Books worth reading
Communications, Signal Processing, and SystemsQilian Liang · 2012
Adaptive and Natural Computing AlgorithmsMarco Tomassini · 2013
Intelligent Science and Intelligent Data EngineeringJian Yang · 2013
Coding the MatrixPhilip N. Klein · 2013
Advances in Neural Networks – ISNN 2013Chengan Guo · 2013
Computer Vision – ACCV 2012Kyoung Mu Lee · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Low-rank approximation In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank. More precisely, it is a minimization…
- Principal component regression In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form…
- Matrix Chernoff bound For certain applications in linear algebra, it is useful to know properties of the probability distribution of the largest eigenvalue of a finite sum of…
- Semidefinite programming Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified…
- Matrix completion Matrix completion is the task of filling in the missing entries of a partially observed matrix, which is equivalent to performing data imputation in statistics…
- Functional principal component analysis Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this…
- MINQUE In statistics, the theory of minimum norm quadratic unbiased estimation (MINQUE) was developed by C. R. Rao. MINQUE is a theory alongside other estimation…
- Bernstein inequalities (probability theory) In probability theory, Bernstein inequalities give bounds on the probability that the sum of random variables deviates from its mean. In the simplest case…