Spectral Learning of Large Structured HMMs for Comparative Epigenomics

Autor:	Zhang, Chicheng, Song, Jimin, Chen, Kevin C, Chaudhuri, Kamalika
Rok vydání:	2015
Předmět:	Statistics - Machine Learning Computer Science - Learning Mathematics - Statistics Theory Quantitative Biology - Genomics
Druh dokumentu:	Working Paper
Popis:	We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types. A natural model for chromatin data in one cell type is a Hidden Markov Model (HMM); we model the relationship between multiple cell types by connecting their hidden states by a fixed tree of known structure. The main challenge with learning parameters of such models is that iterative methods such as EM are very slow, while naive spectral methods result in time and space complexity exponential in the number of cell types. We exploit properties of the tree structure of the hidden states to provide spectral algorithms that are more computationally efficient for current biological datasets. We provide sample complexity bounds for our algorithm and evaluate it experimentally on biological data from nine human cell types. Finally, we show that beyond our specific model, some of our algorithmic ideas can be applied to other graphical models. Comment: 27 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1506.01744 Zobrazit plný text záznamu View this record from Arxiv