datasets:ior
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| - | ==== Innsbruck Object Relation Dataset ==== | ||
| - | This dataset contains the set of possible object-object spatial relations. Learning object-object relations is a difficult problem with sparse, noisy, corrupted and incomplete information which makes it an interesting and challenging machine learning problem. We formulate this problem as the problem of learning missing edges in a multigraph. | ||
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| - | **Keywords**: missing link prediction, data imputation, matrix completion, recommender systems, low-rank approximation | ||
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| - | {{ :research:projects:squirrel:edges.png?600 |}} | ||
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| - | The learning scenario based on which this database was created is a toy clean-up task in a room of kids, where an | ||
| - | agent needs to plan how to transform a messy child's room into a tidy room by moving objects to their storage locations and creating order. An agent can integrate knowledge of possible spatial relations of objects into the planning process and use it to update the world model. Large numbers of objects and their potential interactions in this scenario make this task a large-scale problem. Estimating the missing relations based on those already known can accelerate planning procedures. | ||
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| - | {{research:projects:squirrel:table.png?300}} {{:research:projects:squirrel:drawing.png?300|}} | ||
| - | {{:research:projects:squirrel:m99.jpg?300|}} | ||
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| - | **Dataset Features** | ||
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| - | *The dataset is based on the [[http://shape.cs.princeton.edu/benchmark/|Princeton Shape Benchmark database]]. | ||
| - | *The dataset contains 4 sets for the each possible connection between objects (//in//, //on//, //below// and //next to//). The problem is formulated that all possible relations should be treated so two objects can have multiple connections. | ||
| - | *Links between objects are determined by values: | ||
| - | ***0** - no connection | ||
| - | ***1** - direct connection | ||
| - | ***-1** - reverse connection | ||
| - | ***empty** - unknown connection | ||
| - | * You can download the dataset [[https://iis.uibk.ac.at/public/databases/esann2015/|here]]. | ||
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| - | **Reference** | ||
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| - | Please cite this paper if you use this dataset: | ||
| - | <html> | ||
| - | <div class="li CONF" id="Krivic-2015-ESANN" title="Krivic-2015-ESANN"><p><span class="author"><span class="firstname">Senka</span> <span class="surname">Krivić</span></span>, <span class="author"><span class="firstname">Sandor</span> <span class="surname">Szedmak</span></span>, <span class="author"><span class="firstname">Hanchen</span> <span class="surname">Xiong</span></span>, <span class="author"><span class="firstname">Justus</span> <span class="surname">Piater</span></span>, <span class="parttitle">Learning missing edges via kernels in partially-known | ||
| - | graphs. </span><a href="https://www.elen.ucl.ac.be/esann/">European Symposium on Artificial Neural | ||
| - | Networks, Computational Intelligence and Machine | ||
| - | Learning</a>, <span class="pubdate">2015</span>. <span class="actions"> <a href="https://iis.uibk.ac.at/public/papers/Krivic-2015-ESANN.pdf">[PDF]</a> <a href="javascript:void(0)" onclick="showHide('Krivic-2015-ESANN', 'Abstract')">[Abstract]</a> <a href="javascript:void(0)" onclick="showHide('Krivic-2015-ESANN', 'BibTeX')">[BibTeX]</a></span></p></div> | ||
| - | </html> | ||
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| - | **Acknowledgement** | ||
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| - | This research has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme 3,Information and Communication Technologies) under grant agreement no. 610532, [[http://www.squirrel-project.eu/|Squirrel]] and no. 270273, [[http://www.xperience.org/|Xperience]]. | ||
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| - | **Contact** | ||
| - | [[senka.krivic@uibk.ac.at]] | ||
datasets/ior.txt · Last modified: 2018/09/03 19:35 (external edit)