Approximate Large-scale Multiple Kernel k-means Using Deep Neural Network
Authors: Yueqing Wang, Xinwang Liu, Yong Dou, Rongchun Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our algorithm consumes less time than most comparatively similar algorithms, while it achieves comparable performance with MKC algorithms. |
| Researcher Affiliation | Academia | Yueqing Wang, Xinwang Liu , Yong Dou, Rongchun Li National Laboratory for Parallel and Distributed Processing, NUDT, Changsha, China, 410073 xinwangliu@nudt.edu.cn |
| Pseudocode | Yes | Algorithm 1: Training stage |
| 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 evaluate our algorithm on eight datasets detailed in Table 1. ... such as mnist10k, cifar100-10k, Oxford 102 Category Flowers (102flowers), and birds200... Caltech256, cifar100, mnist, and Image Net... For all datasets, we use two 4096-dimensional features extracted using the Alexnet model [Krizhevsky et al., 2012] and Visual Geometry Group-19 (VGG19) model [Simonyan and Zisserman, 2014] to represent the images. |
| Dataset Splits | No | The paper mentions a 'training stage' using a sampled 'subset' and a 'testing stage' applying the trained network to the 'whole dataset', but it does not specify explicit train/validation/test dataset splits with proportions or counts for reproducible evaluation. |
| Hardware Specification | Yes | All the algorithms reported this paper are performed on a workstation with a 32-core Intel E5-2650 2.00 GHz processor and 256 GB memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | Our network includes one 1D convolutional layer, one max pooling layer, and four fully connected layers. ... The corresponding loss function of our network can be written as follows: JH(θ) = fθ(X) Hsub 2 + θ F , ... To regress Hsub, we use stochastic gradient descent (SGD) method to minimize the loss function. |