A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm

1. A k-Nearest Neighbor Based Multi-InstanceMulti-Label Learning Algorithm kobe chai kobe@kobe.co.il כריית מידע,המכללה האקדמית להנדסה ירושלים

2. Mountains Multi-label learning Trees Lake ?What is Multi-Label Objects example: natural scene image

3. (a) Traditional supervised learning (b) Multi-instance learning (c) Multi-label learning (d) Multi-instance multi-label learning

4. MIML explanations In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels

5. Using example - MIML

6. Using example - MIML person name movie character name movie name game name book name ... Harry Potter Hobbies Friend network books Search behavior movies

7. To Address the Ambiguity Multiple labels Sport? Fans? Football? Training? ………

8. The Problems In MIML Identification process may lose useful information encoded in training examples and therefore be harmful to the learning algorithm’s performance.

9. information loss during degeneration process! An instance of a car – the MIML algorithm can’t recognize this specific car Because of the change below. kNN-MIML will consider the citer (blue door) and will learn the new car.

10. THE PROPOSED METHOD MIML-kNN is proposed for MIML by utilizing the popular k nearest neighbor techniques. Given a test example, MIML-kNN not only considers its neighbors, but also considers its citers which regard it as their own neighbors.

11. Inputs: Equation #1: Equation #2:

12. Outputs: Equation #5:

13. Process:

14. Process Diagram Test Point Training Data (D) Learning Algorithm f Loss Function

15. How it works? Learns one binary classifier for each label Outputs the union of their predictions Can do ranking if classifier outputs scores LimitationDoes not consider label relationships +1 or -1?

16. Experiment Summery The performance of MIML-kNN is compared with MIMLBOOST and MIMLSVM on two real-world MIML tasks. The scene classification data contains 2,000 natural scene images. All the possible class labels are desert, mountains, sea, sunset and trees. average number of labels per image is 1.24±0.44. http://lamda.nju.edu.cn/datacode/mimlimage-data.htm.

17.   Label  Set   #Images      | Label  Set #Images        | Label  Set       #Images-----------------------------------------------------------------------------------------------------      desert   340 | desert+sunset 21  | sunset+trees      28      mountains 268 | desert+trees    20   | desert+mountains+sunset 1        sea 341 | mountains+sea  38   | desert+sunset+trees     3      sunset   216 | mountains+sunset   19   | mountains+sea+trees   6      trees      378 | mountains+trees     106 | mountains+sunset+trees   1desert+mountains   19 | sea+sunset   172 | sea+sunset+trees 4desert+sea 5 | sea+trees       14  |   Total      2,000-----------------------------------------------------------------------------------------------------------------