In the first stage, codebook-based local appearance features are regularized and reduced in dimension using latent topic models, combined with spatial pyramid matching based spatial layout features, and fed into logistic regression classifiers to produce an initial patch level labeling.
By means of the trial results obtained on real-world data, the structure has been shown to regularize highly available feature selection and prediction methods, such as Significant Analysis of Microarray (SAM), Information gain (IG) and the Lasso-type prediction model.
The proposed representative features and TGDR regularized estimation provide an effective way of reducing the dimensionality, accounting for the interactions among genes within the same modules, and, more importantly, accounting for the interactions among modules.
Based on the results of maximizing the RDE, we develop a supervised feature extraction algorithm called regularized discriminant entropy analysis (RDEA).
In fact, L1 regularized logistic regression model inherently performs feature selection.
We initially established the importance of feature selection using a Naive Bayes classifier and subsequently used LASSO regularized multinomial logistic regression to systematically rank the feature weights.
In order to seamlessly capture the similarity between feature descriptors, we perform shape clustering on mid-level features that are generated via graph regularized sparse coding.
In an effort to coherently capture the similarity between feature descriptors, we use multiclass support vector machines for 3D shape classification on mid-level features that are learned via graph regularized sparse coding.
The first stage of the algorithm uses a PLSA topic model to provide regularized dimensionality reduction of its vector quantized input features, feeding the resultant posterior topic probabilities into individual-patch-level Logistic Regression Classifiers (LRCs).
These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process.
