adaboost features selection from large pool

asked 2013-05-28 03:41:59 -0600

amd_best gravatar image

updated 2013-05-28 03:43:01 -0600

Hi, I want to train adaboost with 10^9 features but it can't stay all to gheter in memory , i procedeed with this approch: for(i=1 to 1000) { -load 10^6 features from the 10^9 pool -select the best 1 features and store it
} but if i test the strong classifier on validation set i have results(correct detection-false positive) worst then this approches: -load 10^6 features from the 10^9 pool -select the best 1000 features and store it

i think the first approches is more exaustive then the second but the second is better on validation set, someone can tell me anything about this anomaly?

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you have 10^6 observation of features? How long it is your single feature vector?

yes123 gravatar imageyes123 ( 2013-05-28 06:48:49 -0600 )edit

i want create a ranking of most important features of pedestrian , the pool is very large because i use a features simil to haar, and i can generate a large pool of features, the lenght of vector features descriptor is 10^6 byte

amd_best gravatar imageamd_best ( 2013-05-28 07:52:46 -0600 )edit
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