15 papers · ranked by Valyu relevance
Carl H Lubba, Sarab S Sethi, Philip Knaute, Simon R Schultz + 2 more
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through…
Ling Huang, Jingyi Wang, Qionghua He, Chu Li + 3 more
Global effects of FBA are generally limited to stimuli sharing the same or similar features, as hypothesized in the “feature-similarity gain model”. Visual perception, however, often reflects categories acquired via experience; whether the global-FBA effect can be induced by the categorized features remains unclear.…
Rahi Jain, Wei Xu
Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper approaches primarily focus on shortening the path to find an…
Tümay Capraz, Wolfgang Huber
A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new, synthetic coordinates, and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability and modularity. A common criterion for unsupervised…
Michelle R. Greene, Bruce C. Hansen
Human scene categorization is characterized by its remarkable speed. While many visual and conceptual features have been linked to this ability, significant correlations exist between feature spaces, impeding our ability to determine their relative contributions to scene categorization. Here, we employed a whitening…
Soukhin Das, G.R. Mangun, Mingzhou Ding
Perceptual expertise and attention are two important factors that enable superior object recognition and task performance. While expertise enhances knowledge and provides a holistic understanding of the environment, attention allows us to selectively focus on task-related information and suppress distraction. It has…
Suguru Fujita, Yasuaki Karasawa, Ken-ichi Hironaka, Y-h. Taguchi + 1 more
High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually…
Ngan Thi Dong, Megha Khosla
The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection(FS) methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal…
Rahi Jain, Wei Xu
Feature selection (FS) reduces the dimensions of high dimensional data. Among many FS approaches, ensemble-based feature selection (EFS) is one of the commonly used approaches. The rank aggregation (RA) step influences the feature selection of EFS. Currently, the EFS approach relies on using a single RA algorithm to…
Rahi Jain, Wei Xu
Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step of EFS where results from multiple models are pooled to estimate feature importance. However, the literature…
Majid Mohammadi, Hossein Sharifi Noghabi, Ghosheh Abed Hodtani, Habib Rajabi Mashhadi
One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this…
Philipp Kaniuth, Florian P. Mahner, Jonas Perkuhn, Martin N. Hebart
Perceived similarity offers a window into the mental representations underlying our ability to make sense of our visual world, yet, the collection of similarity judgments quickly becomes infeasible for larger datasets, limiting their generality. To address this challenge, here we introduce a computational approach that…
Luke Ternes, Mark Dane, Marilyne Labrie, Gordon Mills + 3 more
Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenging since there are many obstacles, including segmentation and identifying subcellular compartments for feature extraction.…
Alexander I. Hsu, Eric A. Yttri
The motivation, control, and selection of actions comprising naturalistic behaviors remains a tantalizing but difficult field of study. Detailed and unbiased quantification is critical. Interpreting the positions of animals and their limbs can be useful in studying behavior, and significant recent advances have made…
Tarun Khajuria, Kadi Tulver, Jaan Aru
Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables…