4
PCA 공간에 새로운 벡터를 투영하는 방법?
주성분 분석 (PCA)을 수행 한 후 PCA 공간에 새 벡터를 투영하려고합니다 (즉, PCA 좌표계에서 해당 좌표를 찾습니다). 를 사용하여 R 언어로 PCA를 계산했습니다 prcomp. 이제 내 벡터에 PCA 회전 행렬을 곱할 수 있어야합니다. 이 매트릭스의 주요 구성 요소를 행 또는 열로 배열해야합니까?
21
r
pca
r
variance
heteroscedasticity
misspecification
distributions
time-series
data-visualization
modeling
histogram
kolmogorov-smirnov
negative-binomial
likelihood-ratio
econometrics
panel-data
categorical-data
scales
survey
distributions
pdf
histogram
correlation
algorithms
r
gpu
parallel-computing
approximation
mean
median
references
sample-size
normality-assumption
central-limit-theorem
rule-of-thumb
confidence-interval
estimation
mixed-model
psychometrics
random-effects-model
hypothesis-testing
sample-size
dataset
large-data
regression
standard-deviation
variance
approximation
hypothesis-testing
variance
central-limit-theorem
kernel-trick
kernel-smoothing
error
sampling
hypothesis-testing
normality-assumption
philosophical
confidence-interval
modeling
model-selection
experiment-design
hypothesis-testing
statistical-significance
power
asymptotics
information-retrieval
anova
multiple-comparisons
ancova
classification
clustering
factor-analysis
psychometrics
r
sampling
expectation-maximization
markov-process
r
data-visualization
correlation
regression
statistical-significance
degrees-of-freedom
experiment-design
r
regression
curve-fitting
change-point
loess
machine-learning
classification
self-study
monte-carlo
markov-process
references
mathematical-statistics
data-visualization
python
cart
boosting
regression
classification
robust
cart
survey
binomial
psychometrics
likert
psychology
asymptotics
multinomial