图 书 馆
稀疏滤波Sparse Filtering |
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| 课程网址: | http://videolectures.net/nips2011_ngiam_sparse/ |
| 主讲教师: | Jiquan Ngiam |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification. However, many existing feature learning algorithms are hard to use and require extensive hyperparameter tuning. In this work, we present sparse filtering, a simple new algorithm which is efficient and only has one hyperparameter, the number of features to learn. In contrast to most other feature learning methods, sparse filtering does not explicitly attempt to construct a model of the data distribution. Instead, it optimizes a simple cost function -- the sparsity of L2-normalized features -- which can easily be implemented in a few lines of MATLAB code. Sparse filtering scales gracefully to handle high-dimensional inputs, and can also be used to learn meaningful features in additional layers with greedy layer-wise stacking. We evaluate sparse filtering on natural images, object classification (STL-10), and phone classification (TIMIT), and show that our method works well on a range of different modalities. |
| 关 键 词: | 特征学习; 音频分类; 超参数调整 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-26：cwx |
| 阅读次数: | 141 |