@inproceedings{discovery1433231,
           pages = {252 -- 259},
           title = {Independent Process Analysis without A Priori Dimensional Information},
            note = {This is the authors' accepted version of this published article. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-74494-8\_32},
          volume = {4666},
          editor = {ME Davies and CJ James and SA Abdallah and MD Plumbley},
       publisher = {Springer-Verlag Heidelberg},
       booktitle = {Independent Component Analysis and Signal Separation: Proceedings of the 7th International Conference, ICA 2007, London, UK, September 9-12, 2007},
           month = {September},
            year = {2007},
          author = {P{\'o}czos, B and Szabo, Z and Kiszlinger, M and L{\Ho}rincz, A},
        abstract = {Recently, several algorithms have been proposed for independent subspace analysis where hidden variables are i.i.d. processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden processes. Our claim is supported by numerical simulations. As an illustrative application where the dimensions of the hidden variables are unknown, we search for subspaces of facial components.},
             url = {http://dx.doi.org/10.1007/978-3-540-74494-8\%5f32}
}