[jira] Updated: (MAHOUT-396) Proposal for Implementing Hidden Markov Model

["Max Heimel (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-396?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Max Heimel updated MAHOUT-396:
------------------------------

    Attachment: MAHOUT-396.diff

Patch containing the full HMM implementation. This includes implementations of forward, backward, Viterbi algorithm and three learning algorithms (supervised, Viterbi, Baum-Welch). All algorithms are available in a normal and log-scaled variant. The HMM model can now be exported to JSON.  
This patch also contains a small demo application that implements a POS tagger using the HMM implementation. Using the test/training data sets from http://flexcrfs.sourceforge.net/#Case_Study this demo-application achieves a tagging accuracy of 94% by applying supervised learning. The learning and tagging process takes less than a second on a 2,6Ghz AMD dual core processor with 5Gb of RAM running Ubuntu 10.04.

> Proposal for Implementing Hidden Markov Model
> ---------------------------------------------
>
>                 Key: MAHOUT-396
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-396
>             Project: Mahout
>          Issue Type: New Feature
>    Affects Versions: 0.4
>            Reporter: Max Heimel
>            Assignee: Max Heimel
>            Priority: Minor
>             Fix For: 0.4
>
>         Attachments: MAHOUT-396.diff, MAHOUT-396.diff, MAHOUT-396.diff, MAHOUT-396.diff, MAHOUT-396.diff
>
>
> h4. Overview
> This is a project proposal for a summer-term university project to write a (sequential) HMM implementation for Mahout. Five students will work on this project as part of a course mentored by Isabel Drost.
> h4. Abstract:
> Hidden Markov Models are used in multiple areas of Machine Learning, such as speech recognition, handwritten letter recognition or natural language processing. A Hidden Markov Model (HMM) is a statistical model of a process consisting of two (in our case discrete) random variables O and Y, which change their state sequentially. The variable Y with states {y_1, ... , y_n} is called the "hidden variable", since its state is not directly observable. The state of Y changes sequentially with a so called - in our case first-order - Markov Property. This means, that the state change probability of Y only depends on its current state and does not change in time. Formally we write: P(Y(t+1)=y_i|Y(0)...Y(t)) = P(Y(t+1)=y_i|Y(t)) = P(Y(2)=y_i|Y(1)). The variable O with states {o_1, ... , o_m} is called the "observable variable", since its state can be directly observed. O does not have a Markov Property, but its state propability depends statically on the current state of Y. 
> Formally, an HMM is defined as a tuple M=(n,m,P,A,B), where n is the number of hidden states, m is the number of observable states, P is an n-dimensional vector containing initial hidden state probabilities, A is the nxn-dimensional "transition matrix" containing the transition probabilities such that A[i,j]=P(Y(t)=y_i|Y(t-1)=y_j) and B is the mxn-dimensional "observation matrix" containing the observation probabilties such that B[i,j]= P(O=o_i|Y=y_j).
> Rabiner [[1|My Page#reference1]] defined three main problems for HMM models:
> # Evaluation: Given a sequence O of observations and a model M, what is the probability P(O|M)  that sequence O was generated by model M. The Evaluation problem can be efficiently solved using the Forward algorithm
> # Decoding: Given a sequence O of observations and a model M, what is the most likely sequence Y*=argmax(Y) P(O|M,Y) of hidden variables to generate this sequence. The Decoding problem can be efficiently sovled using the Viterbi algorithm.
> # Learning: Given a sequence O of observations, what is the most likely model M*=argmax(M)P(O|M) to generate this sequence.  The Learning problem can be efficiently solved using the Baum-Welch algorithm.
> The target of each milestone is defined as the implementation for the given goals, the respective documentation and unit tests for the implementation.
> h4.Timeline
> Mid of May 2010 - Mid of July 2010
> h4.Milestones
> I) Define an HMM class based on Apache Mahout Math package offering interfaces to set model parameters, perform consistency checks, perform output prediction.
> 1 week from May 18th till May 25th.
> II) Write sequential implementations of forward (cf. problem 1 [[1|My Page#reference1]]) and backward algorithm.
> 2 weeks from May 25th till June 8th.
> III) Write a sequential implementation of Viterbi algorithm (cf. problem 2 [[1|My Page#reference1]]), based on existing forward algorithm implementation.
> 2 weeks from June 8th till June 22nd
> IV) Have a running sequential implementation of Baum-Welch algorithm for model parameter learning (application II [ref]), based on existing forward/backward algorithm implementation.
> 2 weeks from June 8th till June 22nd
> V) Provide a usage example of HMM implementation, demonstrating all three problems.
> 2 weeks from June 22nd till July 6th
> VI) Finalize documentation and implemenation, clean up open ends.
> 1 week from July 6th till July 13th
> h4.References:
> {anchor:reference1}[[1|http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf]]    Lawrence R. Rabiner (February 1989). "A tutorial on Hidden Markov Models and selected applications in speech recognition". Proceedings of the IEEE 77 (2): 257-286. doi:10.1109/5.18626.
> Potential test data sets:
> [http://www.cnts.ua.ac.be/conll2000/chunking/]
> [http://archive.ics.uci.edu/ml/datasets/Character+Trajectories]

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