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Shonan Seminar: Dimensionality and Scalability (II)

August 1st, 2015 Comments off

In June, Shonan Village in Japan hosted a seminar dedicated to diving into the issues surrounding high intrinsic data dimensionality, distance concentration, similarity search and scalability. It was an amazing opportunity to spend a few days brainstorming these topics with the leading experts in the field and hear about many new surprising results.

We are all looking forward to some new projects and collaborations that were set up during the seminar.

A big shout out to the organizers who did an amazing job and the participants with their individual contributions.

The official report from the seminar can be found here.

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Image Hub Explorer: Journal Paper

August 26th, 2014 Comments off

We were notified today that the extended version of the paper that we have presented at last year’s European Conference on Machine Learning has been accepted for publications in the Multimedia Tools and Applications journal. The paper is titled “Image Hub Explorer: Evaluating Representations and Metrics for Content-based Image Retrieval and Object Recognition“. The full text of the article will soon be available online on the publications page.

The paper is about the Image Hub Explorer system for interactive evaluation and visualization of the utility of various image feature representations and metrics from the perspective of the semantic consistency of the top-k result sets and the emergence of beneficial and/or detrimental image hubs in the data. Indeed, our results indicate that different image feature representations have different levels of susceptibility to the hubness phenomenon and the curse of dimensionality. In the paper, we have examined the quantized bag-of-feature representations for SIFT, SURF, ORB and BRIEF descriptors, though the system itself was build to be applicable to generic representations as well, including DeCaf and similar learned feature types.

The system implements state-of-the-art hubness-aware machine learning methods for metric learning, ranking and classification, as well as several novel visualization layers and components. It will be made freely available in about a month as a part of the Hub Miner library that is to be released soon as open source. We will post more notifications soon.

A video of the demo of Image Hub Explorer is available here.

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A publication in Knowledge and Information Systems Journal

January 15th, 2013 Comments off

I’ve just got a paper published in the Knowledge and Information Systems (KAIS) journal. It proposes a novel secondary similarity measure, tailored specifically for high-dimensional data, under the assumption of hubness. The experimental results are quite encouraging and the analysis shows many interesting properties. The overall percentage of label mismatches is reduced, the occurrence profile purity is increased – and the overall classifier performance is therefore significantly better under the new measure. If you are working with similarity-based methods in high-dimensional data, it is worth checking it out:

The Electronic Version

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About me

July 7th, 2011 Comments off

So, this is going to be my web page, apparently, the place where I will upload some of my research papers and discuss topics of interest. As you have already figured out, I am a computer scientist and I’m currently working on my PhD in machine learning, at the Jožef Stefan Institute in Ljubljana, Slovenia. My interests span into other topics as well, such as stochastic optimization, artificial life, AI, game theory, social network analysis, bio-informatics, as well as some more mathematically rich areas like dynamical systems, chaos theory, graph theory, etc.

I was born in Serbia, in a lovely city of Novi Sad (mostly known for its summer festival – Exit), where I finished my basic education – and where I graduated in informatics in 2008. For my success during the studies, as well as my graduation thesis, I was awarded the Aleksandar Saša Popović award of excellence. Along with my interests in mathematics and computer science, I was also into biology back then, doing some field work and mostly studying insect populations. I have participated in various educational seminars and courses at the Petnica Science Center, by both attending them as a highschool student and lecturing there when I was older. I have also had the opportunity to lead a short project at the Višnjan summer school for gifted highschool kids which was titled Evolution in the Core – a Journey Through the Basics of Artificial Life.

I am presently working on some high dimensional phenomena in real world data, focusing mostly on the phenomenon of hubness, which is an aspect of the dimensionality curse pertaining to nearest neighbor methods in general, both classification and clustering. I am constantly able to find new applications and new ways of exploiting it, so it is an exciting new research area. If you have any comments/ideas/proposals related to this or machine learning in general, do not hesitate to get in touch.

And so it begins…

Cheers,

Nenad

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