Semi-Supervised Transfer Learning with Genetic Algorithm Tuned Transformation and Novel Label Transfer Mechanism

Robotics and intelligent sensing methods are experiencing a new wave applications through the use of machine learning systems. Intelligence is being introduced in robots and sensor platforms by utilizing machine learning techniques such as classification. In the field of robotics, generating training data can be very complex and often, expensive. In this set-up, transfer learning can greatly improve the performance of a classifier wherever and whenever enough labeled data is not available in a domain of interest (target domain), but ample labeled data can be found in a different but related domain (source domain). A new optimized method is proposed in this work to transform the observation from source domain along with a new label transfer mechanism. The transformed, or adapted, domain has the same number of features as the target domain and the same number of observations from the source domain. Labels are transferred from source to target domain using a multivariate Gaussian mixture model (GMM). Genetic algorithm is used to optimize the transformation process by minimizing a cost function that addresses both distribution difference and accuracy. Experiments show that the proposed method outperforms any classifier trained only with source or target domain data.

[1]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[2]  Motoaki Kawanabe,et al.  Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.

[3]  Peter Stone,et al.  Boosting for Regression Transfer , 2010, ICML.

[4]  Ivan Marsic,et al.  Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels , 2013, ICML.

[5]  Saeid Nahavandi,et al.  A linear quadratic optimal motion cueing algorithm based on human perception , 2014, ICRA 2014.

[6]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[7]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Masashi Sugiyama,et al.  Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation , 2008, SDM.

[9]  Peng Hao,et al.  Transfer learning using computational intelligence: A survey , 2015, Knowl. Based Syst..

[10]  Qiang Yang,et al.  Cross-domain sentiment classification via spectral feature alignment , 2010, WWW '10.

[11]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Chee Peng Lim,et al.  A review on otolith models in human perception , 2016, Behavioural Brain Research.

[13]  Jian Sun,et al.  A Practical Transfer Learning Algorithm for Face Verification , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[16]  Jie Lu,et al.  Long term bank failure prediction using Fuzzy Refinement-based Transductive Transfer learning , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[17]  Chee Peng Lim,et al.  A Particle Swarm Optimization-based washout filter for improving simulator motion fidelity , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[18]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[19]  Jie Lu,et al.  Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking Ecosystem , 2014, IEEE Transactions on Industrial Informatics.

[20]  Daumé,et al.  Frustratingly Easy Semi-Supervised Domain Adaptation , 2010 .

[21]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[22]  Trevor Darrell,et al.  What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.

[23]  Hal Daumé,et al.  Frustratingly Easy Domain Adaptation , 2007, ACL.

[24]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[25]  Robert E. Mercer,et al.  Sequential Inductive Transfer for Coronary Artery Disease Diagnosis , 2007, 2007 International Joint Conference on Neural Networks.

[26]  Jürgen Schmidhuber,et al.  Transfer learning for Latin and Chinese characters with Deep Neural Networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[27]  Shady M. K. Mohamed,et al.  Semicircular canal modeling in human perception , 2017, Reviews in the neurosciences.

[28]  Saeid Nahavandi,et al.  Future reference prediction in model predictive control based driving simulators , 2016, ICRA 2016.

[29]  Yifan Gong,et al.  Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[30]  Saeid Nahavandi,et al.  Human Perception-Based Washout Filtering Using Genetic Algorithm , 2015, ICONIP.

[31]  Philip S. Yu,et al.  Domain Invariant Transfer Kernel Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.

[32]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[33]  Saeid Nahavandi,et al.  An optimal washout filter based on genetic algorithm compensators for improving simulator driver perception , 2015 .

[34]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[35]  Rama Chellappa,et al.  Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.

[36]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[37]  Reinaldo A. C. Bianchi,et al.  Using Cases as Heuristics in Reinforcement Learning: A Transfer Learning Application , 2011, IJCAI.

[38]  Yan Liu,et al.  Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning , 2011, 2011 IEEE 11th International Conference on Data Mining.

[39]  Ivor W. Tsang,et al.  Domain Transfer SVM for video concept detection , 2009, CVPR 2009.

[40]  Rich Caruana,et al.  Multitask Learning , 1997, Machine-mediated learning.

[41]  Saeid Nahavandi,et al.  MPC-based motion cueing algorithm with short prediction horizon using exponential weighting , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[42]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[43]  Koby Crammer,et al.  Analysis of Representations for Domain Adaptation , 2006, NIPS.

[44]  Ling Shao,et al.  Transfer Learning for Visual Categorization: A Survey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[45]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[46]  Robert E. Mercer,et al.  The Task Rehearsal Method of Life-Long Learning: Overcoming Impoverished Data , 2002, Canadian Conference on AI.

[47]  J. Heckman.  Sample Selection Bias as a Specification Error (with an Application to the Estimation of Labor Supply Functions) , 1977 .