# Local models and Gaussian mixture models for statistical data processing

@inproceedings{Kambhatla1996LocalMA, title={Local models and Gaussian mixture models for statistical data processing}, author={Nandakishore Kambhatla}, year={1996} }

In this dissertation, we present local linear models for dimension reduction and Gaussian mixture models for classification and regression. When the data has different structure in different parts of the input space, fitting once global model can be slow and inaccurate. Simple learning models can quickly learn the structure of the data in small (local) regions. Thus, local learning techniques can offer us faster and more accurate model fitting. Gaussian mixture models form a soft local model of… CONTINUE READING

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#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 104 REFERENCES

## Nonlinear Image Interpolation using Manifold Learning

VIEW 9 EXCERPTS

HIGHLY INFLUENTIAL

## Solving inverse problems using an EM approach to density estimation

VIEW 8 EXCERPTS

HIGHLY INFLUENTIAL

## Principal components analysis of images via back propagation

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures

VIEW 9 EXCERPTS

HIGHLY INFLUENTIAL

## Asymptotically optimal block quantization

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## Asymptotic Theory for Principal Component Analysis

VIEW 6 EXCERPTS

HIGHLY INFLUENTIAL

## A tutorial on hidden Markov models and selected applications

VIEW 2 EXCERPTS

HIGHLY INFLUENTIAL

## Subspace methods of pattern recognition

VIEW 3 EXCERPTS

HIGHLY INFLUENTIAL