A case for hubness removal in high-dimensional multimedia retrieval

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8416 LNCS, Issue , 2014, Pages 687-692

  Schnitzer, Dominik ^a   Flexer, Arthur ^a   Tomašev, Nenad ^b  

^a AUSTRIAN RESEARCH INSTITUTE FOR ARTIFICIAL INTELLIGENCE   (Austria)

^b JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

high dimensionality; hubness; multimedia retrieval

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

HIGH DIMENSIONAL SPACES; HIGH DIMENSIONALITY; HUBNESS; MEASURING DISTANCES; MULTIMEDIA RETRIEVAL; RETRIEVAL QUALITY; RETRIEVAL SYSTEMS; SIMILARITY-BASED RETRIEVALS;

INFORMATION RETRIEVAL;

EID: 84899952367     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-06028-6_77     Document Type: Conference Paper

References (20)

1. ISMIR 2004 Music Genre Collection, http://ismir2004.ismir.net/genre- contest/index.htm
2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley (1999)
3. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106(1), 59-70 (2007)
4. Flexer, A., Schnitzer, D., Schlüter, J.: A mirex meta-analysis of hubness in audio music similarity. In: Proceedings of the 13th International Society for Music Information Retrieval Conference, pp. 175-180 (2012)
5. Frank, A., Asuncion, A.: UCI machine learning repository (2010), repository located at, http://archive.ics.uci.edu/ml
6. Gouyon, F., Dixon, S., Pampalk, E., Widmer, G.: Evaluating rhythmic descriptors for musical genre classification. In: Proceedings of the 25th International Conference Audio Engineering Society Conference, pp. 196-204 (2004)
7. Houle, M.E., Kriegel, H.-P., Kr̈oger, P., Schubert, E., Zimek, A.: Can Shared-Neighbor Distances Defeat the Curse of Dimensionality. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 482-500. Springer, Heidelberg (2010)
8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91-110 (2004)
9. Mandel, M., Ellis, D.: Song-level features and support vector machines for music classification. In: Proceedings of the 6th International Conference on Music Information Retrieval, London, UK (2005)
10. Nanopoulos, A., Radovanovíc, M., Ivanovíc, M.: How does high dimensionality affect collaborative filtering? In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 293-296 (2009)
11. Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of the Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722-729 (2008)
12. Radovanovíc, M., Nanopoulos, A., Ivanovíc, M.: Hubs in space: Popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research 11, 2487-2531 (2010)
13. Schedl, M.: On the Use of Microblogging Posts for Similarity Estimation and Artist Labeling. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, Utrecht, The Netherlands (August 2010)
14. Schnitzer, D., Flexer, A., Schedl, M., Widmer, G.: Local and global scaling reduce hubs in space. Journal of Machine Learning Research 13, 2871-2902 (2012)
15. Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 370-377 (2005)
16. Tomǎsev, N., Brehar, R., Mladeníc, D., Nedevschi, S.: The influence of hubness on nearest-neighbor methods in object recognition. In: Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing, pp. 367-374 (2011)
17. Tomǎsev, N., Radovanovíc, M., Mladeníc, D., Ivanovíc, M.: The role of hubness in clustering high-dimensional data. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS, vol. 6634, pp. 183-195. Springer, Heidelberg (2011)
18. Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293-302 (2002)
19. Wang, J., Markert, K., Everingham, M.: Learning models for object recognition from natural language descriptions (2009)
20. Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining 5(5), 363-387 (2012)