Dimensionally Reduction: An Experimental Study

Dimensionality reduction as the available method to overcome the “curses of dimensionality” has attracted wide attention. However, the pervious studies treat the visualization and the subsequent classification performance separately. In this case, we do not know whether there is a underlying relationship (i.e., direct proportion) between visualization and the followed classification performance.

[1]  Junbin Gao,et al.  Gaussian Processes Autoencoder for Dimensionality Reduction , 2014, PAKDD.

[2]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[3]  Philip S. Yu,et al.  Bag Constrained Structure Pattern Mining for Multi-Graph Classification , 2014, IEEE Transactions on Knowledge and Data Engineering.

[4]  Y. Takane,et al.  Multidimensional Scaling I , 2015 .

[5]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[8]  Kilian Q. Weinberger,et al.  Learning a kernel matrix for nonlinear dimensionality reduction , 2004, ICML.

[9]  David R. Kaeli,et al.  A MATLAB toolbox for Hyperspectral Image Analysis , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Chris Buckley,et al.  OHSUMED: an interactive retrieval evaluation and new large test collection for research , 1994, SIGIR '94.

[11]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[12]  Geoffrey E. Hinton,et al.  Visualizing Similarity Data with a Mixture of Maps , 2007, AISTATS.

[13]  Junbin Gao,et al.  Supervised Latent Linear Gaussian Process Latent Variable Model for Dimensionality Reduction , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Jia Wu,et al.  Self-adaptive probability estimation for Naive Bayes classification , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[15]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[16]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.

[17]  W. Torgerson.  Multidimensional scaling: I. Theory and method , 1952 .

[18]  E. Aronson,et al.  Theory and method , 1985 .

[19]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[20]  Zhihua Cai,et al.  Attribute Weighting via Differential Evolution Algorithm for Attribute Weighted Naive Bayes (WNB) , 2011 .

[21]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[22]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .