Cluster-weighted modeling
In statistics, cluster-weighted modeling (CWM) is an algorithm-based approach to non-linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld.[1][2]
The basic CWM algorithm gives a single output cluster for each input cluster. However, CWM can be extended to multiple clusters which are still associated with the same input cluster.[3] Each cluster in CWM is localized to a Gaussian input region, and this contains its own trainable local model.[4] It is recognized as a versatile inference algorithm which provides simplicity, generality, and flexibility; even when a feedforward layered network might be preferred, it is sometimes used as a "second opinion" on the nature of the training problem.[5]
The original form proposed by Gershenfeld describes two innovations:
- Enabling CWM to work with continuous streams of data
- Addressing the problem of local minima encountered by the CWM parameter adjustment process[5]
CWM can be used to classify media in printer applications, using at least two parameters to generate an output that has a joint dependency on the input parameters.[6]
References
- ^ Gershenfeld, N. (1997) "Nonlinear Inference and Cluster-Weighted Modeling", Annals of the New York Academy of Sciences, 808, 18–24. doi:10.1111/j.1749-6632.1997.tb51651.x
- ^ Gershenfeld, N., Schoner, B.* & Metois, E. (1999) Cluster-weighted modelling for time-series analysis, Nature, 397 (28 Jan. 1999), 329–332
- ^ Feldkamp, L.A. (2001). "Cluster-weighted modeling with multiclusters" (PDF). International Joint Conference on Neural Networks. 3 (1): 1710–1714.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Boyden, Edward S. "Tree-based Cluster Weighted Modeling: Towards A Massively Parallel Real-Time Digital Stradivarius" (PDF). Cambridge, MA: MIT Media Lab.
{{cite journal}}: Cite journal requires|journal=(help) - ^ a b Prokhorov, A New Approach to Cluster-Weighted Modeling Danil V. "A New Approach to Cluster-Weighted Modeling" (PDF). Dearborn, MI: Ford Research Laboratory.
{{cite journal}}: Cite journal requires|journal=(help); Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Gao, Jun (2003-07-24). "CLUSTER-WEIGHTED MODELING FOR MEDIA CLASSIFICATION". Palo Alto, CA: World Intellectual Property Organization.
{{cite journal}}: Cite journal requires|journal=(help); Unknown parameter|coauthors=ignored (|author=suggested) (help)
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