View source for Data modeling, restructuring, analysis, fuzzy cases, learning

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[[Category:Math on data]]


==Concepts & glossary==

The '''[http://en.wikipedia.org/wiki/Curse_of_dimensionality curse of dimensionality]''' is, roughly, the idea that for each dimension you add to your model, you need proportionally more data for decent training of that model.
Since the volume increases so fast, you will probably have sparsity problems.





===Stochastic processes, deterministic processes, random fields===


A '''deterministic process''' deals with possible determined cases, no unknowns or random variables.


A '''stochastic process''' (a.k.a. '''random process''') allows indeterminacy, typically by working with probability distributions.

A lot of data is stochastically modeled, because you only need partial data and can generally only get partial data.


(Models mixing deterministic and stochastic processes are often called hybrid models)


A '''random field''' basically describes the generalization that we introduced because parameters (dependent variable) are not always time, or one-dimensional, or real-valued.


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A '''conditional random field''' turns up in 

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See also:
* http://en.wikipedia.org/wiki/Stochastic_process
* http://en.wikipedia.org/wiki/Deterministic_system
* http://en.wikipedia.org/wiki/Random_field
* http://en.wikipedia.org/wiki/Conditional_random_field

===Types of problems===

Tasks are often one of:

* '''Clustering''' points out regions or groups of (mutual) similarity, and dissimilarity from other groups.
: clustering may not deal well with future data of the same sort, unless some care has been taken, so may not be the best choice of a learning/predicting system

* '''Vector quantization''': Discretely dividing continuous space into various areas/shapes
: which itself can be used for decision problems, labeling, etc.

* '''Dimensionality reduction''': projecting attributes into lower-dimensional data
: where the resulting data is (hopefully) comparably predictive/correlative (compared to the original)
: The reason is often to eliminate attributes/data that may be irrelevant or too informationally sparse
: see also [[#Ordination.2C_Dimensionality_reduction.2C_Factor_Analysis.2C_Multivariate_analysis]]

* '''Feature extraction''': discovering (a few nice) attributes from (many) old ones, or just from data in general.
: Often has a good amount of overlap with dimensionality reduction

* others...

===Markov property===

the '''Markov property''' is essentially that there is no memory, only direct response: that response of a process is determined entirely by its current state {{comment|(and input, if you don't already define that as part of the state)}}.

More formally, "The environment's response ''(s,r)'' at time ''t+1'' depends only on the Markov state ''s'' and action ''a'' at time ''t''" [http://en.wikipedia.org/wiki/Markov_property]


There are many general concepts that you can make stateless, and thereby Markovian:
* A '''Markov model''' is a [[stochastic model]] with the Markov property

* A '''Markov process''' is a [[stochastic process]] with the Markov property [http://en.wikipedia.org/wiki/Markov_process]

* A '''Markov chain''' refers to a Markov process with finite, countable states [http://en.wikipedia.org/wiki/Markov_chain]

* A '''Markov random field''' [http://en.wikipedia.org/wiki/Markov_random_field]

* A '''Markov logic network'''  [http://en.wikipedia.org/wiki/Markov_logic_network]

* A '''Markov Decision Process (MDP)''' is a decision process that satisfies the Markov property 

* ..etc.

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There are different variants of markov models:
* decision making processes, versus describing system behaviour
* having a complete model of the relevant apsects, or only a partial one.

Basically, describing with a complete model is a markov chain, 
describing with a partial model is a hidden markov model.


The markov chain is the simplest variant.




A state machine is often deterministic 



A markov model is 
sorta-deterministic, 
in that while it says something about state at time t+1 based on state at time t,
it does so with probablistic estimation.

In other words, it aims to make a good guess, does not pretend to be perfect.


Yes, you can make Markov-model implementations of a purely deterministic
state machine, but this mostly proves
* compatibility in that both are discrete-time 
* that the specific cases where the interaction is already known can be modeled by something that
can also be more fuzzy


Similarly, a Markov ''chain'' can be modelled by a finite state machine



Marchov chain is a discrete-time stochastic process



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===Observability and controllability===

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Also related are the concepts of [https://en.wikipedia.org/wiki/Observability observability] criterion and [https://en.wikipedia.org/wiki/Controllability controllability] criterion, introduced by Kalman.

Observability is the idea that the states of a system can be reasonably inferred from knowledge of its outputs.
Roughly, that a good set of sensors lets you build a model that covers the variables.{{verify}}

Controllability is roughly the idea that you can actually guide a system through all states.{{verify}}

Both are related to building something that is likely to be stable in a knowable way.

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===Underfitting and overfitting (learners)===

'''Underfitting''' is when a model is too simple to be good at describing all the patterns in the data. 

Underfitted models and learners may still generalize very well, and that can be intentional, e.g. to describe just the most major patterns.

It may be hard to quantify how crude is too crude, though.



'''Overfitting''' often means the model is allowed to be so complex that a part of it describes all the patterns there are, meaning the rest ends up describing just noise, or insignificant variance or random errors in the training set.

A little overfitting is not disruptive, but a lot of it often is, distorting or drowning out the parts that ''are'' actually modeling the major relationships.

Put another way, overfitting it is the (mistaken) assumption that convergence in the training data means convergence in all data.



There are a few useful tests to evaluate overfitting and underfitting. 

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via cross validation can be a useful test, as it will reveal predictions that aren't even true in other parts of the training data.
It may also help estimate where a good balance lies.


Notes:
* The [https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Bias–variance angle] 
: see e.g. http://datascience.stackexchange.com/questions/361/when-is-a-model-underfitted





For learners
* a pathological case would be to have a model that is an exactly rule-based description of all the training data, with exactly the same complexity as that data


* overfitted learners often means very little generalization

* ...and so has a limited ability to deal with cases that don't look very much like ''specific'' cases in the training set.


Good ways to avoid overfitting depend a little on the type of learner you're using.
If you're halfway involved in the analysis you're doing, and not fully automating analysis,
applied gut feeling tends to avoid most of it already.


See also:
* http://en.wikipedia.org/wiki/Overfitting
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===supervised versus unsupervised systems (learners)===

'''Supervised''' usually means the training process is suggested or somehow (dis)approved. Usually it refers to having annotated trainign data, sometimes to altering it. Example: Classification of documents, basic neural network back-propagation, least-squares fitting, operator cloning

'''Unsupervised''' refers to processes that work without intervention. For example, self-organizing maps, or clustering documents based on similarity needs no annotation.

'''Semi/lightly supervised''' usually means there is an iterative process which needs only minimal human intervention, be it to deal with a few unclear cases, for information that may be useful, or such.


===Inductive versus deductive systems (learners)===
'''Inductive''' refers to training purely from data.

'''Deductive''' refers to also having a theory about the domain domain.


Inductive learning can be approached as a search in a hypothesis space. <!--
The hypothesis space can finitely include all possible hypotheses about the training data, and choose one so that it hypothesizes correctly for every single one. Of course, this is overfitting: It would just be a description of all of the data. 

FIND-S is a simple algorithm to work a little better. It comes down to:
* looking for the most specific hypothesis (or hypotheses; consider that these could ignore parameters) consistent with the trainign data. 
* Based on conjunction of constraints
* If the training data converges, this should find a simple hypothesis to apply to unseen data with the same attributes.
* Problem: It finds only one, and not necessarily the best for anything but the training data 
* Noise will easily disturb into too general hypothesis
* Introduces '''restriction bias''' in that we can have hypothesises with only discrete-valued attributes and can only specify a specific value or "don't care," not a subset of possible values, in a single hypothesis.

This can be generalized into also having disjunction, negation and more, but this ends up in a mess: Only the training data could be unambiguously classified, because it is our training data. Unseen data can be classified with a plethora of hypotheses, each of which are as valid as the other. The only vaguely viable thing would be to take the subset of all valid hypotheses and use that somehow, but this is not a good solution. 


This teaches that we in fact need bias to be able to generalize, usually through something refered to as '''inductive bias''', which is basically the `inate' set of assumptions a learner uses. Algorithms will usually differ in types of bias, so when this bias matches that of the data the algorithm will work better for that data.

It also means that the more accurate prior knowledge you have, the higher the quality of the learning - which makes intuitive sense. On the other hand, embedding too much prior knowledge will dismiss what is seen by it to be unusual data.

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===Model-based versus model-free systems (learners)===
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When you create a model of what to learn and where from, this can save a lot of computation over you-figure-it-out solutions (which may not even be feasilbe), but will often not be adaptive to things that don't fit that model well.

Put another way, that model will have all the assumptions you put into it, for better ''and'' worse. 


Model-free often makes for a lot more possibilities and a lot more optimization work.
But takes a lot more time. See e.g. fancier neural net training easily being GPU-weeks.

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===Transfer learning===
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When you have an already-trained model that you want to adapt to a similar task,
you would probably like to use that already-refined knowledge.

In some cases this can be straightforward parameters, 
in others it's more about monitoring that the new model stays performing decently on the old task.



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==Statistical modeling==
Data fusion means merging data from various sources, e.g. various sensors. 
This often implies some sort of modelling.


===Regression analysis===
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Regression analysis models the relationship between one dependent and one (or more) independent variables, via as-of-yet-unknown parameters.

For each independent variable, it estimates a function (the regression function) mapping it to the dependent variable, with one or more parameters (as deemed sensible).

While there are many variants, people easily relate it to fairly linear and simple models, in part because 


Often estimates the [[conditional expectation]]
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Regression glossary:
* '''linear''' regression models a linear predictor function
: typically a least squares estimator

* '''simple''' regression indicates a single independent/explanatory variable
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* Ordinary least squares [https://en.wikipedia.org/wiki/Ordinary_least_squares]

* total lease squares [https://en.wikipedia.org/wiki/Total_least_squares]
: generalization of Deming (and orthogonal) regression

* Deming regression [https://en.wikipedia.org/wiki/Deming_regression]
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====Linear regression====
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====Logistic regression====
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Regresssion model where the dependent variable is [[categorical variable|categorical]] -- often specifically binary, as more-categories are handled by multinomial logistic regression (if unordered) or ordinal logistic regression (if ordered).

https://en.wikipedia.org/wiki/Logistic_regression
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Multinomial logistic regression

https://en.wikipedia.org/wiki/Multinomial_logistic_regression


Ordinal logistic regression

https://en.wikipedia.org/w/index.php?title=Ordinal_logistic_regression
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===Mean Squared Error (metric)===
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Measures the difference between the estimator/estimation and what is estimated from.

A bunch of data modelling tries to minimize MSE.

https://en.wikipedia.org/wiki/Mean_squared_error
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==State observers==

See [[Optimization_theory,_control_theory#State_observers_.2F_state_estimation.3B_filters]]

==Classifiers==


===Combining classifiers===
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Combining classifiers can be useful when you think some may have imperfections that sometimes leads to strange answers for certain data. 

Averaging can sometimes make sense. There is also focus on:
* Voting: let each model predict, and look for consensus: use the most common answer.

* Bagging: Voting, but use same learner on different training data, to the end of hoping to lessen [[overfitting]].

* Boosting: Focus on misclassified data by weighing it more the next iteration


Also: 
* Use classification to self-teach or 
* co-teach by generating corpora yourself. May easily delude itself.
* Add a human for difficult cases only (like co-training)
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=Semi-sorted=

 




[[Category:Algorithms]]
[[Category:Math on data]]
[[Category:Machine Learning]]

=Unsorted=
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Domain adaptation 


Zero-Shot Learning (ZSL) is when a learner is expected to learn a new class 
without having explicitly been shown examples. It is an extreme form of form of domain adaptation.

One-shot learning 

Few-show learning

...where classically we have large dataset of human-labeled 


Zero-shot learning still typically counts on auxiliary information, and knowledge transfer.
Consider how as a human, you might be able to visually identify a platypus, never having seen one,
but only descriptions.






(k-)Nearest Neighbour, a.k.a. kNN

Not the same as k-means. TODO: See [http://www.ir.iit.edu/~dagr/DataMiningCourse/Spring2001/Notes/Clustering.pdf] for distinction.

TODO: See [http://www.google.com/search?q=%22k-nearest%22%20neighbor%20%22k-means%22], [http://people.revoledu.com/kardi/tutorial/KNN/index.html], [http://www.autonlab.org/autonweb/software/10408.html], [http://www.dbs.informatik.uni-muenchen.de/~boehm/publications/dexa03-final.pdf]





* various at http://crd.lbl.gov/~cding/, http://ecology.msu.montana.edu/labdsv/R/, http://www.statsoft.com/textbook/stcluan.html

* [http://video.google.com/videoplay?docid=2546753955666121345 Google Video: Clustering aggregation (Aristides Gionis)]

* VQ / LVQ ((Learning) Vector Quantization)
** (apparently like kNN and SOM)

* Curvilinear Component Analysis (CCA)

* [http://en.wikipedia.org/wiki/Expectation-maximization_algorithm Expectation Maximization] 

* Quality Threshold (QT)
** Doesn't cluster elements deemed low quality


* Fuzzy ([[http://public.fh-wolfenbuettel.de/~hoeppnef/clustering.html]], [http://documents.wolfram.com/applications/fuzzylogic/Manual/12.html], [http://fuzzy.iau.dtu.dk/tutor/ddfm_files/v3_document.htm])


* Spectral Clustering http://crd.lbl.gov/~cding/Spectral/notes.html


* BSAS, see maybe [http://www-staff.it.uts.edu.au/~paulk/DMBio.cgi/pubs/paul-clustering.pdf]

* Artificial neural network (ANN)
* Self-organizing map (SOM) (sometimes Kohonen Map, or Self Organizing Feature Map) [http://www.sims.berkeley.edu/~hearst/irbook/10/node5.html]
** Usually used for reducing dimensionality 

* Shi-Malik
* Meila-Shi


* Discriminant Function Analysis (DFA, DA)


Trained Structure
Classifier


Adaptive Resonance Theory


Linear programming [http://www-unix.mcs.anl.gov/otc/Guide/faq/linear-programming-faq.html#Q1] [http://www.google.com/search?q=Integer%20Linear%20Programming]


*RIPPER



Tile coding: simple formalism to have learning affect similar states {{verify}}








* Jain, ''Murty, Data Clustering: A Review,'' (1999), [http://citeseer.csail.mit.edu/jain99data.html]
* [http://en.wikipedia.org/wiki/Data_clustering Wikipedia: Data clustering]
* http://www.kddresearch.org/Courses/Fall-2001/CIS732/Lectures/

* http://www.autonlab.org/tutorials/index.html


Software:
* [http://bips.u-strasbg.fr/fr/Documentation/ClustalX/ ClustalX]
* [http://evolution.genetics.washington.edu/phylip.html Phylip]


Unsorted:
http://citeseer.csail.mit.edu/722717.html

http://citeseer.csail.mit.edu/718017.html

http://citeseer.csail.mit.edu/guha98cure.html

http://citeseer.csail.mit.edu/chu02incremental.html

http://citeseer.csail.mit.edu/steinbach00comparison.html

http://citeseer.csail.mit.edu/karypis99chameleon.html

http://citeseer.csail.mit.edu/fred03robust.html

http://citeseer.csail.mit.edu/712324.html

http://citeseer.csail.mit.edu/topchy03combining.html

http://citeseer.csail.mit.edu/711453.html

http://citeseer.csail.mit.edu/706677.html

http://citeseer.csail.mit.edu/jain01structure.html

http://citeseer.csail.mit.edu/719978.html

http://citeseer.csail.mit.edu/714801.html

http://citeseer.csail.mit.edu/guha00rock.html

http://citeseer.csail.mit.edu/na01performance.html

http://citeseer.csail.mit.edu/260668.html

http://citeseer.csail.mit.edu/ng94efficient.html

http://citeseer.csail.mit.edu/cutting92scattergather.html

http://citeseer.csail.mit.edu/steinbach00comparison.html

http://citeseer.csail.mit.edu/fred02data.html

http://citeseer.csail.mit.edu/law04simultaneous.html

http://fuzzy.iau.dtu.dk/tutor/ddfm_files/v3_document.htm

http://citeseer.csail.mit.edu/masulli02soft.html


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