41 results about "Regularized least squares" patented technology

Techniques are disclosed that implement algorithms for rapidly finding the leave-one-out (LOO) error for regularized least squares (RLS) problems over a large number of values of the regularization parameter λ. Algorithms implementing the techniques use approximately the same time and space as training a single regularized least squares classifier / regression algorithm. The techniques include a classification / regression process suitable for moderate sized datasets, based on an eigendecomposition of the unregularized kernel matrix. This process is applied to a number of benchmark datasets, to show empirically that accurate classification / regression can be performed using a Gaussian kernel with surprisingly large values of the bandwidth parameter σ. It is further demonstrated how to exploit this large σ regime to obtain a linear-time algorithm, suitable for large datasets, that computes LOO values and sweeps over λ.

A method and system use capacity-approaching rateless code to communicate multimedia data even with very short codewords, such as 64 bits or less, via erasure and noise channels. The method provides a way to design the edge degree distribution of rateless codes for any arbitrary channel. Based on an equivalent metric of decoding behavior in any channels, the degree distribution of a rateless code such as Luby-transform codes and raptor codes is optimized based on in-process status of decoding. A regularized least-squares optimization is used to avoid erroneous decoding. Multiple feedbacks can further improve the performance.

The invention discloses a time-frequency domain modal parameter identification method based on nonlinear frequency modulation component decomposition, comprising the following steps: 1. Obtaining the dynamic response signal of the structure to be identified and setting the sampling time and frequency; 2. Through Fourier The leaf series establishes the redundant Fourier model of the response signal, instantaneous frequency and instantaneous amplitude; 3. Extract the instantaneous frequency information of the response signal through the generalized parameterized time-frequency transformation; 4. According to the obtained instantaneous frequency information, through regularization The instantaneous amplitude information of the response signal is extracted by the least squares method; 5. According to the slope information of the instantaneous frequency and the logarithm of the instantaneous amplitude, the modal parameter identification of the structure is realized by using the linear least squares fitting algorithm; 6. The identification results are analyzed Error Analysis. The invention directly uses the vibration response of the structure for signal analysis and mode parameter identification, is simple and convenient to use, can effectively improve the mode parameter identification accuracy of dense mode structures, and has strong applicability and anti-interference ability.

The invention discloses an image compression method based on active learning and semi-supervised learning, which selects color information of some representative pixel points in an image to learn a model which can predict colors of the remaining pixel points in the image on the basis of the active learning. The image compression method mainly includes the following steps that all pixel points of the image are provided, the image is segmented into a plurality of rectangular areas simply, one pixel point is respectively and randomly selected from each rectangular area, the pixel points are learned actively so as to select the most representative pixel points, grey level values and color values of the most representative pixel points are recorded, grey level values of the remaining pixel points of the image are only recorded, and a compression process is completed. During decompression, Laplace regularized least squares (LapRLS) serving as a semi-supervised learning algorithm is used for predicting and restoring colors of all pixel points.