Analysis of fast structured dictionary learning

Analysis of Fast Alternating Minimization for Structured Dictionary Learning

Methods exploiting sparsity have been popular in imaging and signal processing applications including compression, denoising, and imaging inverse problems. Data-driven approaches such as dictionary learning enable one to discover complex image features from datasets and provide promising performance over analytical models. Alternating minimization algorithms have been particularly popular in di...

Structured Dictionary Learning for Classification

Sparsity driven signal processing has gained tremendous popularity in the last decade. At its core, the assumption is that the signal of interest is sparse with respect to either a fixed transformation or a signal dependent dictionary. To better capture the data characteristics, various dictionary learning methods have been proposed for both reconstruction and classification tasks. For classifi...

Learning structured visual dictionary for object tracking

Online Group-Structured Dictionary Learning Supplementary Material

Lemma 1. If ρ = 0, then Mt = M0 +M′t (∀t ≥ 1). When ρ > 0, then Mt = M ′ t (∀t ≥ 1). Proof. 1. Case ρ = 0: Since γt = 1 (∀t ≥ 1), thus Mt = M0 + ∑t i=1 Ni. We also have that ( i t 0 = 1 (∀i ≥ 1), and therefore M′t = ∑t i=1 Ni, which completes the proof. 2. Case ρ > 0: The proof proceeds by induction. • t = 1: In this case γ1 = 0, M1 = 0 × M0 + N1 = N1 and M′1 = N1, which proves that M1 = M ′ 1....

Collaborative Filtering via Group-Structured Dictionary Learning

Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented method outperforms its state-of-the-art competitors and has several adv...