Affine Dispersers from Subspace Polynomials Eli Ben-Sasson∗ Swastik Kopparty †

Source URL: www.math.ias.edu

PROBLEM FIELD: APPROXIMATION OF FRAMES BY NORMALIZED TIGHT ONES MICHAEL FRANK Let {xi }i be a frame of a Hilbert subspace K ⊆ H of a given (separable) Hilbert space H with upper and lower frame bounds B and A. The resu

Source URL: www.imn.htwk-leipzig.de

Functional Subspace Clustering with Application to Time Series A. Proof of the statement in Eq. (3) In order to show the the result in Eq. (3), we breakdown P the process in Eq. (2) into two steps: Let us denote e =

Source URL: jmlr.org

• Mathematics
• Functions and mappings
• Transformation
• Abstract algebra
• Linear algebra
• Linear map
• Quaternions

FORMALIZED MATHEMATICS 2007, Vol. 15, No. 3, Pages

Source URL: fm.mizar.org

• Algebra
• Mathematics
• Linear algebra
• Linear map
• Vector space
• Basis
• Linear subspace
• Rank
• Linear combination
• Operator
• Dual space
• Kernel

Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data

Source URL: haxbylab.dartmouth.edu

ASYMPTOTIC PERFORMANCE ANALYSIS OF PCA ALGORITHMS BASED ON THE WEIGHTED SUBSPACE CRITERION Jean Pierre Delmas, Victor Gabillon TELECOM & Management SudParis, 91011 Evry, France D´epartement CITI, CNRS UMR 5157 ABSTRACT

Source URL: victorgabillon.nfshost.com

• Multivariate statistics
• Dimension reduction
• Principal component analysis

Functional Subspace Clustering with Application to Time Series Mohammad Taha Bahadori∗ MOHAMMAB @ USC . EDU David Kale∗† DKALE @ USC . EDU

Source URL: jmlr.org

• Machine learning
• Statistics
• Learning
• Artificial intelligence
• Multivariate statistics
• Dynamic programming
• Cluster analysis
• Data mining
• Geostatistics
• K-means clustering
• Principal component analysis
• Dynamic time warping

IEEE PEM December 2014 Feature (Deepak Divan) -Power Electronics at the Grid Edge25-14)

Source URL: groups.csail.mit.edu

• Machine learning
• Artificial intelligence
• Learning
• Ensemble learning
• Cognition
• Random subspace method
• Supervised learning
• Statistical classification

Algorithms and Hardness for Subspace Approximation Amit Deshpande∗ Madhur Tulsiani† Nisheeth K. Vishnoi‡

Source URL: ttic.uchicago.edu

Subspace Rings: A New Tool in Communication Trellis Design John Kieffer Dept. of Electrical & Computer Engineering University of Minnesota Minneapolis, MN 55455

Source URL: ita.ucsd.edu