Partially labeled classification with Markov random walks
Partially labeled classification with Markov random walks
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发表时间：
2001-01
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通讯作者：
M. Szummer;T. Jaakkola
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作者：
M. Szummer;T. Jaakkola
为了对大量未标记样本进行分类，我们将有限数量的已标记样本与未标记样本上的马尔可夫随机游走表示相结合。随机游走表示以稳健、概率的方式利用数据中的任何低维结构。我们开发并比较了几种适合于这种表示的估计标准/算法。这尤其包括具有允许闭合形式解的平均边际标准的多路分类。随机游动的时间尺度规则化了表示，并且可以通过有利于明确分类的基于裕度的标准来设置。我们还通过调整个别例子的时间尺度来扩展这一基本正则化。我们在合成实例和文本分类问题上演示了该方法。
To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random walk representation exploits any low dimensional structure in the data in a robust, probabilistic manner. We develop and compare several estimation criteria/algorithms suited to this representation. This includes in particular multi-way classification with an average margin criterion which permits a closed form solution. The time scale of the random walk regularizes the representation and can be set through a margin-based criterion favoring unambiguous classification. We also extend this basic regularization by adapting time scales for individual examples. We demonstrate the approach on synthetic examples and on text classification problems.