计算机科学
神经编码
人工智能
词典学习
K-SVD公司
机器学习
编码(社会科学)
基础(线性代数)
信号处理
模式识别(心理学)
集合(抽象数据类型)
趋同(经济学)
在线机器学习
稀疏逼近
无监督学习
数学
数字信号处理
统计
几何学
计算机硬件
经济
程序设计语言
经济增长
作者
Julien Mairal,Francis Bach,Jean Ponce,Guillermo Sapiro
标识
DOI:10.1145/1553374.1553463
摘要
Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.
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