Non-stationary data sequence classification using online class priors estimation
Pattern Recognition
Transfer estimation of evolving class priors in data stream classification
Pattern Recognition
Speaker Tracking Using Recursive EM Algorithms
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
| Hi-index | 754.84 |
The problem of estimating the prior probabilitiesq = (q_{1} cdots q_{m-1})ofmstatistical classes with known probability density functionsF_{1}(X) cdots F_{m}(x)on the basis ofnstatistically independent observations(X_{l} cdots x_{n})is considered. The mixture densityg(x|q) = sum^{m-1}_{j-1}q_{j}F_{j}(x) + (1 - sum^{m-1}_{tau = 1}q_{tau})F_{m(x)is used to show that the maximum likelihood estimate ofqis asymptotically efficient and weakly consistent under very mild constraints on the set of density functions. A recursive estimate is proposed forq. By using stochastic approximation theory and optimizing the gain sequence, it is shown that the recursive estimate is asymptotically efficient for them = 2class case. Form > 2classes, the rate of convergence is computed and shown to be very close to asymptotic efficiency.