13,592 research outputs found
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Sparse representation based classification (SRC) methods have achieved
remarkable results. SRC, however, still suffer from requiring enough training
samples, insufficient use of test samples and instability of representation. In
this paper, a stable inverse projection representation based classification
(IPRC) is presented to tackle these problems by effectively using test samples.
An IPR is firstly proposed and its feasibility and stability are analyzed. A
classification criterion named category contribution rate is constructed to
match the IPR and complete classification. Moreover, a statistical measure is
introduced to quantify the stability of representation-based classification
methods. Based on the IPRC technique, a robust tumor recognition framework is
presented by interpreting microarray gene expression data, where a two-stage
hybrid gene selection method is introduced to select informative genes.
Finally, the functional analysis of candidate's pathogenicity-related genes is
given. Extensive experiments on six public tumor microarray gene expression
datasets demonstrate the proposed technique is competitive with
state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table
Bounded Coordinate-Descent for Biological Sequence Classification in High Dimensional Predictor Space
We present a framework for discriminative sequence classification where the
learner works directly in the high dimensional predictor space of all
subsequences in the training set. This is possible by employing a new
coordinate-descent algorithm coupled with bounding the magnitude of the
gradient for selecting discriminative subsequences fast. We characterize the
loss functions for which our generic learning algorithm can be applied and
present concrete implementations for logistic regression (binomial
log-likelihood loss) and support vector machines (squared hinge loss).
Application of our algorithm to protein remote homology detection and remote
fold recognition results in performance comparable to that of state-of-the-art
methods (e.g., kernel support vector machines). Unlike state-of-the-art
classifiers, the resulting classification models are simply lists of weighted
discriminative subsequences and can thus be interpreted and related to the
biological problem
Supervised cross-modal factor analysis for multiple modal data classification
In this paper we study the problem of learning from multiple modal data for
purpose of document classification. In this problem, each document is composed
two different modals of data, i.e., an image and a text. Cross-modal factor
analysis (CFA) has been proposed to project the two different modals of data to
a shared data space, so that the classification of a image or a text can be
performed directly in this space. A disadvantage of CFA is that it has ignored
the supervision information. In this paper, we improve CFA by incorporating the
supervision information to represent and classify both image and text modals of
documents. We project both image and text data to a shared data space by factor
analysis, and then train a class label predictor in the shared space to use the
class label information. The factor analysis parameter and the predictor
parameter are learned jointly by solving one single objective function. With
this objective function, we minimize the distance between the projections of
image and text of the same document, and the classification error of the
projection measured by hinge loss function. The objective function is optimized
by an alternate optimization strategy in an iterative algorithm. Experiments in
two different multiple modal document data sets show the advantage of the
proposed algorithm over other CFA methods
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