Translated Learning:Transfer learning across different feature spacesWenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. In Proceedings of Twenty-Second Annual Conference on Neural Infor-mation Processing Systems (NIPS 2008)

Definition Transfer Learning across different feature spaces When labeled data are more insufficient in target feature space than in other feature spaces.

E.g. Web data(text document > images), cross-language classification(English > Bangla, or other languages..)

Human Learning Example Task: tyrannosaurus vs stegosaurus - tyrannosaurus: bipedal carnivore with a mas-

sive skull balanced by long, heavy tail. Its fore-limbs were small and retained only two digits.

- stegosaurus: quadruped ornithischian dinosaur of four long bony spikes on a flexible tail and two rows of upright triangular bony plates running along the back.

Model-level Translation

LearningInput Output

LearningInput Output

Elephants are big mammals on earth...massivehoofedmammalof Africa... translating learning

models

make the best use of available data that have both features of the source and target domains

Model-level Translation A language model to link the class labels to the features in the source spaces, which is translated to the features in the target spaces. It is com-pleted by tracing back to the instances in the tar-get spaces

𝑐 𝑦 𝑠 𝑦 𝑡 𝑥𝑡Feature-level translator

Features in source space

Features in target space

𝑐 𝑦 𝑠 𝑥𝑠 𝑐 𝑦 𝑡 𝑥𝑡

Translated Learning Classify the instances as accurately as possible using the labeled training data and the translator .

Elephants are big mammals on earth...

massivehoofedmammalof Africa...

source spacelabeled unlabeledlabeled

target space

𝑝 (𝑦𝑡|𝑦 𝑠 )∝𝜙 (𝑦 𝑡 , 𝑦 𝑠)

𝑥𝑠=(𝑦𝑠1 ,…, 𝑦 𝑠

𝑛𝑠) 𝑥𝑡 𝑥𝑢ℒ𝑠 ℒ𝑡 𝒰

feature translator

Risk Minimization Frame-work Risk function measure the risk for classifying to the category .

Loss function loss with respect to the event of and being rele-

vant.

the label of is Models with respect to and involving all the possible models

Distance function between two models and

Model Estimation Approximate the risk function as

Assume there is no prior difference among all the classes

This prior balances the influence of different classes in the class-imbalance case

Translated Learning via Risk Min-imization

Training phase For each

Test phase For each

Predict the label ,

Source space labeled data Target space labeled data

𝑐 𝑦 𝑠 𝑦 𝑡 𝑥𝑡𝜃𝑐 𝜃𝑥𝑡

Feature Translator 𝑥𝑠 𝑥𝑡

𝑦 𝑠 𝑦 𝑡

Source(text)Target(images)

Instance

Feature social annotations on images

Search engine results in response to queries

Web pages including text and pictures

If we use instance-level co-occurrence data,

EvaluationText-aided Image Classification

Images from Caltech-256, Documents from ODP(Open Directory Project) Auxiliary data: binary labeled text documents Objective: Image classification when co-occurrence data is insuffi-cient Evaluated under three dissimilarity functions

Kullback-Leibler divergence(KL), Negative of cosine function(NCOS), Negative of the Pear-son’s correlation coefficient(NPCC)

Co-occurrence data collected from a image search engine,

> >

EvaluationText-aided Image Classification

(the size of )

EvaluationText-aided Image Classification

the classification model relies more on the auxiliary text training data

EvaluationCross-language Classification

Dataset from ODP English/German pages English documents are used to help classify German documents only 16 German labeled documents are available in each category co-occurrence data: English-German dictionary, NCOS is used for dissimilarity function Assume that machine translation is unavailable and they rely on dictionary only

EvaluationCross-language Classification

when is small, the performance of TLRisk is better and sta-ble (

Conclusions A translated learning framework for classifying tar-get data using data from another feature space. We can find a bridge to link the two spaces with only a little labeled data in the target space. They formulated translated learning framework us-ing risk minimization and an approximation method for model estimation . Showed effectiveness through two applications, the text-aided image classification and the cross-lan-guage classification.

Thank you !

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