A k nearest neighbor based multi instance multi label learning algorithm
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A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm. kobe chai kobe@kobe.co.il. כריית מידע,המכללה האקדמית להנדסה ירושלים. Mountains. Multi-label learning. Trees. Lake. ? What is Multi-Label Objects. example: natural scene image.

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

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A k nearest neighbor based multi instance multi label learning algorithm

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

kobe chai

kobe@kobe.co.il

,


What is multi label objects

Mountains

Multi-label learning

Trees

Lake

?What is Multi-Label Objects

example: natural scene image


A k nearest neighbor based multi instance multi label learning algorithm

(a) Traditional supervised learning

(b) Multi-instance learning

(c) Multi-label learning

(d) Multi-instance multi-label learning


Miml explanations

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


Using example miml

Using example - MIML


Using example miml1

Using example - MIML

person name

movie character name

movie name

game name

book name

...

Harry

Potter

Hobbies

Friend network

books

Search behavior

movies


To address the ambiguity

To Address the Ambiguity

Multiple labels

Sport? Fans? Football? Training?


The problems in miml

The Problems In MIML

Identification process may lose useful information encoded in

training examples and therefore be harmful to the learning algorithms performance.


A k nearest neighbor based multi instance multi label learning algorithm

information loss during degeneration process!

An instance of a car the MIML algorithm cant recognize this specific car

Because of the change below.

kNN-MIML will consider the citer (blue door) and will learn the new car.


The proposed method

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.


Inputs

Inputs:

Equation #1:

Equation #2:


Outputs

Outputs:

Equation #5:


Process

Process:


A k nearest neighbor based multi instance multi label learning algorithm

Process Diagram

Test Point

Training

Data (D)

Learning

Algorithm

f

Loss

Function


1 or 1

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?


Experiment summery

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 1.240.44.

http://lamda.nju.edu.cn/datacode/mimlimage-data.htm.


A k nearest neighbor based multi instance multi label learning algorithm

  • 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-----------------------------------------------------------------------------------------------------------------


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