Bell A J, Sejnowski T J
Computational Neurobiology Laboratory, The Salk Institute, PO Box 85800, San Diego, CA 9186-5800, USA.
Network. 1996 May;7(2):261-7. doi: 10.1088/0954-898X/7/2/005.
Unsupervised learning algorithms paying attention only to second-order statistics ignore the phase structure (higher-order statistics) of signals, which contains all the informative temporal and spatial coincidences which we think of as 'features'. Here we discuss how an Independent Component Analysis (ICA) algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions. This is illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth. The resulting independent basis functions look like the sounds themselves, having similar temporal envelopes and the same musical pitches. Thus they reflect both the phase and frequency information inherent in the data.
仅关注二阶统计量的无监督学习算法会忽略信号的相位结构（高阶统计量），而相位结构包含了所有我们视为“特征”的信息丰富的时间和空间巧合。在此，我们讨论如何使用独立成分分析（ICA）算法来阐明自然信号的高阶结构，从而得到其独立基函数。通过对用指甲有节奏地轻敲牙齿所发出声音进行ICA变换对此进行了说明。所得的独立基函数看起来与声音本身相似，具有相似的时间包络和相同的音高。因此，它们反映了数据中固有的相位和频率信息。
