Distribution-Based Semi-Supervised Learning for Activity Recognition
Authors: Hangwei Qian, Sinno Jialin Pan, Chunyan Miao7699-7706
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on three public datasets to verify the effectiveness of our method compared with state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | Hangwei Qian, Sinno Jialin Pan, Chunyan Miao School of Computer Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Interdisciplinary Graduate School Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University, Singapore qian0045@e.ntu.edu.sg, {sinnopan, ascymiao}@ntu.edu.sg |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks; it primarily presents mathematical formulations and proofs. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We conduct experiments on 3 sensor-based activity datasets. The statistics are listed in Table 2. ... Skoda (Stiefmeier, Roggen, and Tr oster 2007). ... WISDM uses accelerometer sensors ... (Kwapisz, Weiss, and Moore 2010). HCI composes of gestures ... (F orster, Roggen, and Tr oster 2009). |
| Dataset Splits | Yes | Each dataset is randomly split into 3 subsets: labeled training set, unlabeled training set and test set. Each subset is set to contain activities of all classes. We set the ratio to be 0.02:0.1:0.88 and fix r = 100. |
| Hardware Specification | Yes | The experiments are conducted on a Linux server with Intel(R) Xeon(R) E5-2695 2.40GHz CPU. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We set the ratio to be 0.02:0.1:0.88 and fix r = 100. The impact of differentiating r will be discussed later. Different from experimental setups in existing papers that set labeled data s ratio to be quite large (Matsushige, Kakusho, and Okadome 2015; Stikic, Larlus, and Schiele 2009), we deliberately set the labeled data s ratio to be extremely small. ... We adopt RBF kernels for all the kernels used in the experiments. |