`

Timezone: »

 
Poster
Metric Learning with Multiple Kernels
Jun Wang · Huyen T Do · Adam Woznica · Alexandros Kalousis

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

Metric learning has become a very active research field. The most popular representative--Mahalanobis metric learning--can be seen as learning a linear transformation and then computing the Euclidean metric in the transformed space. Since a linear transformation might not always be appropriate for a given learning problem, kernelized versions of various metric learning algorithms exist. However, the problem then becomes finding the appropriate kernel function. Multiple kernel learning addresses this limitation by learning a linear combination of a number of predefined kernels; this approach can be also readily used in the context of multiple-source learning to fuse different data sources. Surprisingly, and despite the extensive work on multiple kernel learning for SVMs, there has been no work in the area of metric learning with multiple kernel learning. In this paper we fill this gap and present a general approach for metric learning with multiple kernel learning. Our approach can be instantiated with different metric learning algorithms provided that they satisfy some constraints. Experimental evidence suggests that our approach outperforms metric learning with an unweighted kernel combination and metric learning with cross-validation based kernel selection.

Author Information

Jun Wang (University of Geneva)
Huyen T Do (University of Geneva)
Adam Woznica (University of Geneva)
Alexandros Kalousis (University of Applied Sciences, Western Switzerland)

More from the Same Authors

  • 2020 Poster: Goal-directed Generation of Discrete Structures with Conditional Generative Models »
    Amina Mollaysa · Brooks Paige · Alexandros Kalousis
  • 2018 : Poster Session 1 + Coffee »
    Tom Van de Wiele · Rui Zhao · J. Fernando Hernandez-Garcia · Fabio Pardo · Xian Yeow Lee · Xiaolin Andy Li · Marcin Andrychowicz · Jie Tang · Suraj Nair · Juhyeon Lee · Cédric Colas · S. M. Ali Eslami · Yen-Chen Wu · Stephen McAleer · Ryan Julian · Yang Xue · Matthia Sabatelli · Pranav Shyam · Alexandros Kalousis · Giovanni Montana · Emanuele Pesce · Felix Leibfried · Zhanpeng He · Chunxiao Liu · Yanjun Li · Yoshihide Sawada · Alexander Pashevich · Tejas Kulkarni · Keiran Paster · Luca Rigazio · Quan Vuong · Hyunggon Park · Minhae Kwon · Rivindu Weerasekera · Shamane Siriwardhanaa · Rui Wang · Ozsel Kilinc · Keith Ross · Yizhou Wang · Simon Schmitt · Thomas Anthony · Evan Cater · Forest Agostinelli · Tegg Sung · Shirou Maruyama · Alexander Shmakov · Devin Schwab · Mohammad Firouzi · Glen Berseth · Denis Osipychev · Jesse Farebrother · Jianlan Luo · William Agnew · Peter Vrancx · Jonathan Heek · Catalin Ionescu · Haiyan Yin · Megumi Miyashita · Nathan Jay · Noga H. Rotman · Sam Leroux · Shaileshh Bojja Venkatakrishnan · Henri Schmidt · Jack Terwilliger · Ishan Durugkar · Jonathan Sauder · David Kas · Arash Tavakoli · Alain-Sam Cohen · Philip Bontrager · Adam Lerer · Thomas Paine · Ahmed Khalifa · Ruben Rodriguez · Avi Singh · Yiming Zhang
  • 2018 : Coffee Break and Poster Session I »
    Pim de Haan · Bin Wang · Dequan Wang · Aadil Hayat · Ibrahim Sobh · Muhammad Asif Rana · Thibault Buhet · Nicholas Rhinehart · Arjun Sharma · Alex Bewley · Michael Kelly · Lionel Blondé · Ozgur S. Oguz · Vaibhav Viswanathan · Jeroen Vanbaar · Konrad Żołna · Negar Rostamzadeh · Rowan McAllister · Sanjay Thakur · Alexandros Kalousis · Chelsea Sidrane · Sujoy Paul · Daphne Chen · Michal Garmulewicz · Henryk Michalewski · Coline Devin · Hongyu Ren · Jiaming Song · Wen Sun · Hanzhang Hu · Wulong Liu · Emilie Wirbel
  • 2012 Poster: Parametric Local Metric Learning for Nearest Neighbor Classification »
    Jun Wang · Alexandros Kalousis · Adam Woznica