کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
564726 1451751 2014 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Probabilistic sequence clustering with spectral learning
ترجمه فارسی عنوان
توالی احتمالی خوشه بندی با یادگیری طیفی
کلمات کلیدی
فراگیری ماشین، یادگیری طیفی، روش لحظات، خوشه بندی توالی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

In this paper, we derive two novel learning algorithms for time series clustering; namely for learning mixtures of Markov Models and mixtures of Hidden Markov Models. Mixture models are special latent variable models that require the usage of local search heuristics such as Expectation Maximization (EM) algorithm, that can only provide locally optimal solutions. In contrast, we make use of the spectral learning algorithms, recently popularized in the machine learning community. Under mild assumptions, spectral learning algorithms are able to estimate the parameters in latent variable models by solving systems of equations via eigendecompositions of matrices or tensors of observable moments. As such, spectral methods can be viewed as an instance of the method of moments for parameter estimation, an alternative to maximum likelihood. The popularity stems from the fact that these methods provide a computationally cheap and local optima free alternative to EM. We conduct classification experiments on human action sequences extracted from videos, clustering experiments on motion capture data and network traffic data to illustrate the viability of our approach. We conclude that the spectral methods are a practical and useful alternative in terms of computational effort and solution quality to standard iterative techniques such as EM in several sequence clustering applications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 29, June 2014, Pages 1–19
نویسندگان
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