Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Falanqaynta Takoorka Tooska ah

Dib u eeg kala-soocidda LDA iyo odoroska, malo-awaalka wada jirka ah ee la wadaago, cabbirrada takoorka, iyo faraqa PCA-ga.

Wadooyinka haga ee la xidhiidhaFalanqaynta Takoorka Tooska ah
Su'aal 1 ee 8

Why does common LDA classification produce linear decision boundaries?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Why does common LDA classification produce linear decision boundaries?
    • It uses class-specific covariance matrices in every case
    • It maximizes total feature variance without labels
    • Classes share a covariance matrix in its usual Gaussian model
    • It ignores class priors whenever class sizes differ
  2. How does LDA choose projection directions in its supervised reduction role?
    • Maximize total variance without labels
    • Favor class separation relative to within-class variation
    • Choose features only by their marginal variance
    • Keep only the original columns with largest means
  3. With K classes and at least K minus one input features, what upper bound applies to the number of useful LDA discriminant directions?
    • K squared
    • K
    • The number of observations
    • K minus one
  4. Which objective does PCA optimize in contrast to LDA?
    • High overall variance without using class labels
    • Class posterior probability
    • Maximum class separation using labels
    • Minimum classification error at every threshold
  5. When may a low-variance direction matter to LDA?
    • When it has the smallest variance regardless of labels
    • Only when PCA has already chosen it
    • When it separates labeled classes
    • Only when every class has the same mean on it
  6. What changes in quadratic discriminant analysis compared with common LDA?
    • Each class must have the same prior probability
    • Each class may have its own covariance matrix
    • Every class mean is constrained to be equal
    • All quadratic terms are removed from the class comparison
  7. Why can LDA be numerically difficult when features greatly outnumber examples?
    • The number of classes must automatically become larger
    • Class priors cannot be estimated from any labeled sample
    • The shared-covariance assumption removes every conditioning problem
    • Covariance estimates may be singular or unstable
  8. A plot shows separated training groups after LDA projection. What has not yet been established?
    • Reliable generalization on held-out data
    • That labels influenced the projection
    • That a projection was computed
    • That classes were present in training