[Paper Review] Dimensionality Reduction as Probabilistic Inference
Probabilistic Framework to interpret non-probabilistic Algorithms
Dimension Reduction
Dimension Reduction
[Paper Review] Dimensionality Reduction as Probabilistic Inference
Probabilistic Framework to interpret non-probabilistic Algorithms
[Paper Review] Sparse Sufficient Dimension Reduction
Sparse Sufficient Dimension Reduction using Penalty
[Experiment] Non-linear Sufficient Dimension Reduction using quantum kernel
plans to build dimension reduction of quantum version
[Paper Review] Sufficient Dimension Folding for Tensorial Data
Sufficient Dimension Folding Theory and Techniques to reduce the dimension of tensorial data.
[Sufficient Dimension Reduction] Part 4. Central Mean Space
Generalized version of SDR and SIR
[Non-linear Dimension Reduction] Part 3. Central sigma-Field for Nonlinear SDR and GSIR
Generalized version of SDR and SIR
[Non-linear Dimension Reduction] Part 2. Coordinate Representation and KPCA
Non-linear Dimension Reduction - 2. Coordinate Representation and KPCA
[Non-linear Dimension Reduction] Part 1. Reproducing Kernel Hilbert Space
Reproducing Kernel Hilbert Space
[Sufficient Dimension Reduction] Part3. Contour Regression and Directional Regression
Contour regression and Directional Regression
[Sufficient Dimension Reduction] Part2. Advanced algorithm of SIR
Parametric, Kernel Inverse regression, Sliced Average Variance Estimator.
[Sufficient Dimension Reduction] Part1. Introduction and Sliced Inverse Regression
Introduction to SDR and SIR method
[Basic Dimension Reduction] Part4. Advanced PCA
PCA의 응용 기법에 대한 설명
[Basic Dimension Reduction] Kernel Trick을 활용한 알고리즘 (Polynomial Regression,Kernel PCA,SVM)
Kernel Trick을 활용한 High-Dimension Mapping Computing의 간소화
[Basic Dimension Reduction] Part1. Bregman Divergence
Bregman Divergence에 관한 설명
[Basic Dimension Reduction] 군집분석(Cluster Analysis)
분할기법,계층기법,밀도기법에 대한 설명
[Basic Dimension Reduction] PCA,MCA,FA를 활용한 차원축소
PCA,MCA,FA에 대한 대략적인 설명