Self-supervised autoencoders for clustering and classification
Clustering techniques aim at finding meaningful groups of data samples which exhibit similarity with regards to a set of characteristics, typically measured in terms of pairwise distances. Due to the so-called curse of dimensionality, i.e., the observation that high-dimensional spaces are unsuited f...
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| Published in: | Evolving systems Vol. 11; no. 3; pp. 453 - 466 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Springer Berlin Heidelberg
01.09.2020
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| ISSN: | 1868-6478, 1868-6486 |
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| Abstract | Clustering techniques aim at finding meaningful groups of data samples which exhibit similarity with regards to a set of characteristics, typically measured in terms of pairwise distances. Due to the so-called curse of dimensionality, i.e., the observation that high-dimensional spaces are unsuited for measuring distances, distance-based clustering techniques such as the classic
k
-means algorithm fail to uncover meaningful clusters in high-dimensional spaces. Thus, dimensionality reduction techniques can be used to greatly improve the performance of such clustering methods. In this work, we study Autoencoders as Deep Learning tools for dimensionality reduction, and combine them with
k
-means clustering to learn low-dimensional representations which improve the clustering performance by enhancing intra-cluster relationships and suppressing inter-cluster ones, in a self-supervised manner. In the supervised paradigm, distance-based classifiers may also greatly benefit from robust dimensionality reduction techniques. The proposed method is evaluated via multiple experiments on datasets of handwritten digits, various objects and faces, and is shown to improve external cluster quality measuring criteria. A fully supervised counterpart is also evaluated on two face recognition datasets, and is shown to improve the performance of various lightweight classifiers, allowing their use in real-time applications on devices with limited computational resources, such as Unmanned Aerial Vehicles (UAVs). |
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| AbstractList | Clustering techniques aim at finding meaningful groups of data samples which exhibit similarity with regards to a set of characteristics, typically measured in terms of pairwise distances. Due to the so-called curse of dimensionality, i.e., the observation that high-dimensional spaces are unsuited for measuring distances, distance-based clustering techniques such as the classic
k
-means algorithm fail to uncover meaningful clusters in high-dimensional spaces. Thus, dimensionality reduction techniques can be used to greatly improve the performance of such clustering methods. In this work, we study Autoencoders as Deep Learning tools for dimensionality reduction, and combine them with
k
-means clustering to learn low-dimensional representations which improve the clustering performance by enhancing intra-cluster relationships and suppressing inter-cluster ones, in a self-supervised manner. In the supervised paradigm, distance-based classifiers may also greatly benefit from robust dimensionality reduction techniques. The proposed method is evaluated via multiple experiments on datasets of handwritten digits, various objects and faces, and is shown to improve external cluster quality measuring criteria. A fully supervised counterpart is also evaluated on two face recognition datasets, and is shown to improve the performance of various lightweight classifiers, allowing their use in real-time applications on devices with limited computational resources, such as Unmanned Aerial Vehicles (UAVs). |
| Author | Tefas, Anastasios Nousi, Paraskevi |
| Author_xml | – sequence: 1 givenname: Paraskevi orcidid: 0000-0002-3087-3174 surname: Nousi fullname: Nousi, Paraskevi email: paranous@csd.auth.gr organization: Department of Informatics, Aristotle University of Thessaloniki – sequence: 2 givenname: Anastasios surname: Tefas fullname: Tefas, Anastasios organization: Department of Informatics, Aristotle University of Thessaloniki |
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| Cites_doi | 10.1111/j.1469-1809.1936.tb02137.x 10.1109/CVPR.2016.556 10.1016/S0031-3203(02)00060-2 10.1109/NNSP.1999.788121 10.1109/TIT.2014.2375327 10.1109/TNNLS.2014.2329240 10.1609/aaai.v28i1.8916 10.1145/1015330.1015332 10.1007/3-540-44503-X_27 10.1016/j.neucom.2017.05.042 10.1016/j.patrec.2009.09.011 10.1145/1273496.1273562 10.1109/5.726791 10.1007/978-3-642-41822-8_15 10.1109/CVPR.2014.227 10.1016/j.asoc.2015.05.026 10.1145/1014052.1014118 10.1023/A:1009769707641 10.1007/978-3-319-14998-1_17 10.21236/ADA164453 10.1109/TIP.2014.2348868 10.1109/TPAMI.2005.92 10.1016/j.eswa.2012.07.021 10.1016/0098-3004(84)90020-7 10.1145/1015330.1015408 10.14569/IJACSA.2013.040406 10.1145/1273496.1273523 10.1109/CVPR.2015.7298682 10.1007/978-3-319-65172-9_18 10.1002/nav.3800020109 10.1016/j.patrec.2004.04.007 10.1109/ICPR.2014.272 10.1016/j.patcog.2015.02.020 10.1109/34.598228 10.1145/1390156.1390294 |
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| Keywords | Dimensionality reduction Deep learning Clustering Autoencoders Classification |
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| References_xml | – reference: ChrysouliCTefasASpectral clustering and semi-supervised learning using evolving similarity graphsAppl Soft Comput20153462563710.1016/j.asoc.2015.05.026 – reference: Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy c-means algorithms. Int J Adv Comput Sci Appl 4(4) – reference: Passalis N, Tefas A (2016) Information clustering using manifold-based optimization of the bag-of-features representation. IEEE Trans Cybern – reference: Rolfe JT, LeCun Y (2013) Discriminative recurrent sparse auto-encoders. arXiv preprint arXiv:1301.3775 – reference: Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 – reference: Passalis N, Tefas A (2017) Dimensionality reduction using similarity-induced embeddings. IEEE Trans Neural Netw Learn Syst – reference: Song C, Liu F, Huang Y, Wang L, Tan T (2013) Auto-encoder based data clustering. In: Iberoamerican Congress on Pattern Recognition. Springer, pp 117–124 – reference: Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning. ACM, pp 1096–1103 – reference: KuhnHWThe Hungarian method for the assignment problemNRL195521–283977551010.1002/nav.3800020109 – reference: Tian F, Gao B, Cui Q, Chen E, Liu TY (2014) Learning deep representations for graph clustering. In: AAAI, pp 1293–1299 – reference: Wang J, Wang J, Ke Q, Zeng G, Li S (2015) Fast approximate k-means via cluster closures. In: Multimedia Data Mining and Analytics. Springer, pp 373–395 – reference: HuangZExtensions to the k-means algorithm for clustering large data sets with categorical valuesData Min Knowl Disc19982328330410.1023/A:1009769707641 – reference: FisherRAThe use of multiple measurements in taxonomic problemsAnn Eugen19367217918810.1111/j.1469-1809.1936.tb02137.x – reference: Xing EP, Jordan MI, Russell SJ, Ng AY (2003) Distance metric learning with application to clustering with side-information. In: Advances in neural information processing systems, pp 521–528 – reference: SrivastavaNHintonGEKrizhevskyASutskeverISalakhutdinovRDropout: a simple way to prevent neural networks from overfittingJ Mach Learn Res20141511929195832315921318.68153 – reference: Le QV (2013) Building high-level features using large scale unsupervised learning. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 8595–8598 – reference: Nousi P, Tefas A (2017) Deep learning algorithms for discriminant autoencoding. Neurocomputing – reference: MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1. Oakland, CA, USA, pp 281–297 – reference: Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the twenty-first international conference on Machine learning. ACM, p 116 – reference: BoutsidisCZouziasAMahoneyMWDrineasPRandomized dimensionality reduction for k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ k $$\end{document}-means clusteringIEEE Trans Inf Theory201561210451062333276410.1109/TIT.2014.2375327 – reference: LikasAVlassisNVerbeekJJThe global k-means clustering algorithmPattern Recogn200336245146110.1016/S0031-3203(02)00060-2 – reference: Yang J, Parikh D, Batra D (2016) Joint unsupervised learning of deep representations and image clusters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5147–5156 – reference: Dhillon IS, Guan Y, Kulis B (2004) Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 551–556 – reference: Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: Proceedings of the 24th international conference on Machine learning. ACM, pp 209–216 – reference: KhanSSAhmadACluster center initialization algorithm for k-means clusteringPattern Recogn Lett200425111293130210.1016/j.patrec.2004.04.007 – reference: Dehghan A, Ortiz EG, Villegas R, Shah M (2014) Who do i look like? Determining parent-offspring resemblance via gated autoencoders. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1757–1764 – reference: Ding C, Li T (2007) Adaptive dimension reduction using discriminant analysis and k-means clustering. In: Proceedings of the 24th international conference on Machine learning. ACM, pp 521–528 – reference: Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105 – reference: Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, pp 1027–1035 – reference: BelhumeurPNHespanhaJPKriegmanDJEigenfaces vs. fisherfaces: recognition using class specific linear projectionIEEE Trans Pattern Anal Mach Intell199719771172010.1109/34.598228 – reference: Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 – reference: Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. 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