Fast Recursive Low-rank Tensor Learning for Regression
Authors: Ming Hou, Brahim Chaib-draa
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experimental Results In our experiments, the root mean squares of prediction (RMSEP) [Kim et al., 2005] as well as the Q index [Luo et al., 2015] are used to quantitatively gauge the predictive performance of our approach. We recorded the CPU learning time per new mini-batch for all recursive methods, and we also gave CPU learning time for batch methods using the entire training set. We compared RHOPLS with NPLS [Bro, 1996], RNPLS [Eliseyev and Aksenova, 2013], HOPLS [Zhao et al., 2013] and IHOPLS [Hou and Chaib-draa, 2016] on general tensorial sequences with no special structures assumed in contrast to spatio-temporal data. |
| Researcher Affiliation | Academia | Ming Hou and Brahim Chaib-draa Department of Computer Science and Software Engineering, Laval University, Quebec, Canada ming.hou.1@ulaval.ca, brahim.chaib-draa@ift.ulaval.ca |
| Pseudocode | No | The paper describes its method in several “Step” sections (e.g., Step 0: Initial Approximation, Step 1: Incremental Approximation) but these are descriptive paragraphs, not structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We first tested RHOPLS on the Utrecht Multi-Person Motion (UMPM) benchmark [Van Der Aa et al., 2011], which provides the simultaneous recordings of video sequences and 3D ground truth positions of human natural motions in daily life activities. ... the tests were carried out on a benchmark tensor regression application, that is, decoding limb movements from monkey s brain signals using Neurotycho food tracking Electrocorticography (ECo G) dataset [Chao et al., 2010]. |
| Dataset Splits | Yes | One half of the shuffled sequence served as training set while the remaining half was used for test. The optimal hyper-parameters of all methods were determined by crossvalidation, so that their best performance could be exhibited balancing between speed and accuracy. |
| Hardware Specification | Yes | All tests were done on a server of 12 cores 3.20GHz CPU. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers used for its implementation or experiments. |
| Experiment Setup | Yes | For RHOPLS, the initial number of latent vectors F and initial input loadings L and output loadings K are needed to be tuned, so are the incremental loadings L and K. For simplicity, we assumed L = L and K = K just to reduce the number of hyper-parameters, and L, K are tuned by conducting a grid search on the combination of typical values, i.e., for L of a 3rd-order input tensor, we might search on [4, 8], [8,12], [12,16], [16,20] ... and so on. |