Patent Number: 7,788,191

Title: Ordered data compression system and methods using principle component analysis

Abstract: Methods and systems are provided for encoding, transmission and decoding of vectorized input data, for example, video or audio data. A convex invariance learning framework is established for processing input data or a given data type. Each input vector is associated with a variable transformation matrix that acts on the vector to invariantly permute the vector elements. Joint invariance and model learning is performed on a training set of invariantly transformed vectors over a constrained space of transformation matrices using maximum likelihood analysis. The maximum likelihood analysis reduces the data volume to a linear subspace volume in which the training data can be modeled by a reduced number of variables. Principal component analysis is used to identify a set of N eigenvectors that span the linear subspace. The set of N eigenvectors is used a basis set to encode input data and to decode compressed data.

Inventors: Jebara; Tony (New York, NY)

Assignee: The Trustees of Columbia University in the City of New York

International Classification: G06F 15/18 (20060101)

Expiration Date: 8/31/12018